Chủ đề về xe tự lái (autonomous vehicles) và những thách thức trong quản lý chúng đang ngày càng xuất hiện thường xuyên trong các đề thi IELTS Reading hiện đại. Đây là một chủ đề công nghệ đương đại, liên quan đến nhiều khía cạnh xã hội, pháp lý và kỹ thuật – những yếu tố mà IELTS thường đánh giá khả năng đọc hiểu của thí sinh.
Trong bài viết này, bạn sẽ nhận được một đề thi IELTS Reading hoàn chỉnh với 3 passages theo đúng format bài thi thật, từ mức độ dễ đến khó. Cụ thể, bạn sẽ được luyện tập với đề thi đầy đủ 3 passages với độ khó tăng dần (Easy → Medium → Hard), 40 câu hỏi đa dạng bao gồm 7-8 dạng câu hỏi phổ biến giống thi thật, đáp án chi tiết kèm giải thích cặn kẽ từng câu, hệ thống từ vựng quan trọng được phân loại theo từng passage, và các kỹ thuật làm bài hiệu quả từ kinh nghiệm thực tế.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với các dạng câu hỏi khác nhau và nâng cao kỹ năng đọc hiểu học thuật. Hãy dành đủ 60 phút để làm bài trong điều kiện thi thực tế để có kết quả đánh giá chính xác nhất.
Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là một phần thi quan trọng đánh giá khả năng đọc hiểu tiếng Anh học thuật của bạn. Bài thi bao gồm 3 passages với độ dài và độ khó tăng dần, tổng cộng khoảng 2000-2750 từ. Bạn có đúng 60 phút để hoàn thành 40 câu hỏi, không có thời gian thêm để chép đáp án vào phiếu trả lời.
Phân bổ thời gian khuyến nghị như sau:
- Passage 1: 15-17 phút (13 câu hỏi)
- Passage 2: 18-20 phút (13 câu hỏi)
- Passage 3: 23-25 phút (14 câu hỏi)
Lưu ý rằng độ khó tăng dần từ Passage 1 đến Passage 3, vì vậy đừng dành quá nhiều thời gian cho những câu đầu. Mỗi câu trả lời đúng được 1 điểm, không bị trừ điểm với câu sai.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm đầy đủ các dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Câu hỏi trắc nghiệm nhiều lựa chọn
- True/False/Not Given – Xác định thông tin đúng/sai/không đề cập
- Yes/No/Not Given – Xác định quan điểm tác giả
- Matching Headings – Nối tiêu đề với đoạn văn
- Sentence Completion – Hoàn thành câu
- Summary Completion – Hoàn thành đoạn tóm tắt
- Matching Features – Nối thông tin với đặc điểm
- Short-answer Questions – Câu hỏi trả lời ngắn
Mỗi dạng câu hỏi yêu cầu kỹ năng và chiến lược khác nhau, vì vậy hãy đọc kỹ instructions (hướng dẫn) trước khi làm bài.
IELTS Reading Practice Test
PASSAGE 1 – The Dawn of Autonomous Vehicles
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The concept of self-driving cars has fascinated engineers and the public alike for decades. What was once purely the realm of science fiction is now rapidly becoming reality on roads around the world. Autonomous vehicles, or AVs, are cars, trucks, and other forms of transportation that can navigate and operate without human input, using a combination of sensors, cameras, artificial intelligence, and complex algorithms.
The journey toward fully autonomous vehicles has been gradual but steady. Early experiments in the 1980s and 1990s demonstrated that computer-controlled vehicles were theoretically possible, but the technology was far from practical. It wasn’t until the 2000s, particularly after the DARPA Grand Challenge competitions, that serious progress began. These competitions, sponsored by the U.S. Defense Advanced Research Projects Agency, challenged teams to create vehicles that could navigate desert terrain without human control. While the first challenge in 2004 saw no team complete the course, by 2005, five vehicles successfully finished, proving that autonomous navigation was achievable.
Today’s autonomous vehicles are far more sophisticated than those early prototypes. Modern AVs use a technology called LiDAR (Light Detection and Ranging), which creates detailed three-dimensional maps of the vehicle’s surroundings by bouncing laser beams off nearby objects. These vehicles also employ radar systems to detect the speed and distance of other vehicles, multiple cameras to recognize road signs and traffic lights, and advanced GPS systems for precise location tracking. All this data is processed in real-time by powerful onboard computers running complex machine learning algorithms that can make split-second decisions about steering, acceleration, and braking.
The Society of Automotive Engineers has established six levels of vehicle automation, ranging from Level 0 (no automation) to Level 5 (full automation). At Level 0, the human driver does everything. Level 1 includes basic driver assistance features like cruise control. Level 2 vehicles can control both steering and acceleration under certain conditions, but the driver must remain attentive and ready to take over. Level 3 allows the vehicle to handle all aspects of driving in certain situations, though the driver must be prepared to intervene when requested. Level 4 vehicles can drive themselves in most conditions without human input, but may have geographical or environmental limitations. Finally, Level 5 represents complete autonomy – vehicles that can handle all driving tasks under all conditions without any human intervention whatsoever.
Most vehicles currently on the market with “self-driving” features are actually at Level 2 or Level 3. Companies like Tesla, General Motors, and several others offer systems that can maintain lane position, adjust speed based on traffic, and even change lanes on highways. However, these systems require constant driver supervision. True Level 4 and Level 5 vehicles are still primarily in testing phases, though several companies, including Waymo (a subsidiary of Google’s parent company Alphabet), have begun offering limited robotaxi services in selected cities.
The potential benefits of widespread autonomous vehicle adoption are substantial. Proponents argue that AVs could dramatically reduce traffic accidents, since the vast majority of crashes are caused by human error – factors like distraction, fatigue, impaired driving, and poor judgment. Autonomous vehicles don’t get tired, don’t check their phones, and don’t drive under the influence of alcohol or drugs. Computer systems can also react faster than humans to unexpected situations, potentially avoiding collisions that would be unavoidable for human drivers.
Beyond safety, autonomous vehicles promise other advantages. They could improve traffic flow and reduce congestion by communicating with each other and maintaining optimal speeds and following distances. This coordination could also reduce fuel consumption and emissions. For elderly people and those with disabilities who cannot drive, AVs could provide newfound independence and mobility. The technology could also transform public transportation, making it more flexible and accessible. Some experts even envision a future where private car ownership declines as people rely on fleets of shared autonomous vehicles that come when called, reducing the need for parking spaces in crowded urban areas.
However, despite these potential benefits, the path to a future dominated by autonomous vehicles faces significant challenges. Technical obstacles remain, particularly in handling unusual or unexpected situations that fall outside the patterns the vehicles were trained to recognize. Weather conditions like heavy snow or rain can interfere with sensors. Construction zones with altered traffic patterns can confuse navigation systems. And then there are the countless unpredictable elements of real-world driving: a child running into the street, a mattress falling off a truck, a police officer directing traffic with hand signals. While autonomous vehicles have logged millions of miles in testing, critics argue that this is still insufficient to ensure safety in all possible scenarios.
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
-
According to the passage, the DARPA Grand Challenge in 2004
A. was won by five different teams
B. proved that autonomous vehicles were impossible
C. had no teams complete the course successfully
D. took place in an urban environment -
What is the main function of LiDAR technology in autonomous vehicles?
A. To detect the speed of other vehicles
B. To create 3D maps of the surroundings
C. To recognize traffic lights
D. To provide GPS tracking -
According to the passage, Level 3 autonomous vehicles
A. require no human intervention at all
B. can only assist with cruise control
C. need the driver to be ready to take control when asked
D. are fully autonomous in all conditions -
Which company is mentioned as offering robotaxi services in some cities?
A. Tesla
B. General Motors
C. Waymo
D. DARPA -
The passage suggests that most traffic accidents are caused by
A. technical failures in vehicles
B. poor road conditions
C. human error and judgment
D. inadequate traffic laws
Questions 6-9: True/False/Not Given
Do the following statements agree with the information in the passage?
Write:
- TRUE if the statement agrees with the information
- FALSE if the statement contradicts the information
- NOT GIVEN if there is no information on this
-
The first DARPA Grand Challenge competition resulted in several vehicles completing the course.
-
Modern autonomous vehicles use only LiDAR technology for navigation.
-
Level 2 autonomous vehicles are the most common type currently available to consumers.
-
Autonomous vehicles have completely eliminated all technical challenges related to weather conditions.
Questions 10-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
-
Autonomous vehicles use __ to make quick decisions about controlling the vehicle.
-
One major advantage of AVs is that they don’t experience __ like human drivers do.
-
Autonomous vehicles could reduce the need for __ in cities if people share them instead of owning personal cars.
-
Critics believe that despite extensive testing, AVs haven’t driven enough to ensure safety in __.
Công nghệ LiDAR trong xe tự lái tạo bản đồ 3D môi trường xung quanh
PASSAGE 2 – Legal and Ethical Dilemmas in Autonomous Vehicle Regulation
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The rapid development of autonomous vehicle technology has created a regulatory vacuum that governments worldwide are struggling to fill. Traditional traffic laws were written with the assumption that a licensed human driver would be in control of every vehicle on the road. These laws address issues of liability, insurance, and safety standards from a human-centric perspective. The emergence of vehicles that can operate without human input fundamentally challenges this framework, raising complex legal questions that have no easy answers.
One of the most contentious issues concerns liability in the event of an accident involving an autonomous vehicle. When a human-driven car crashes, the question of fault is usually straightforward: the driver, another party, or sometimes the vehicle manufacturer if a mechanical defect was responsible. But when an autonomous vehicle crashes, the situation becomes considerably more ambiguous. Should the owner of the vehicle be held responsible, even if they weren’t driving? Should the manufacturer of the vehicle bear liability? What about the company that designed the autonomous driving software, which might be a different entity from the vehicle manufacturer? Or should the makers of the sensors and hardware components share some responsibility?
This question is not merely theoretical. In 2018, an autonomous Uber vehicle struck and killed a pedestrian in Arizona, marking the first fatality involving a fully autonomous vehicle. The safety driver who was supposed to monitor the system and take control in emergencies was watching a television show on her phone at the time. Eventually, the safety driver was charged with negligent homicide, but the case highlighted the murky nature of responsibility when autonomous systems are involved. Should the driver have been paying attention even though the vehicle was supposed to be driving itself? Was Uber responsible for inadequate training? Did the vehicle’s sensors and decision-making system fail? All these questions complicate the traditional understanding of liability.
Insurance companies face similar dilemmas. The current auto insurance model is based on assessing individual driver risk – considering factors like driving history, age, and location. This model breaks down when vehicles drive themselves. Some experts predict that as autonomous vehicles become more common, the burden of insurance will shift from individual owners to manufacturers and software companies, since they will effectively be responsible for the vehicle’s behavior. However, this shift raises concerns about how to price such insurance and whether manufacturers will be willing to accept potentially enormous liability for millions of vehicles on the road.
Beyond liability and insurance, regulators must grapple with ethical dilemmas that autonomous vehicles present. These vehicles must be programmed to make decisions in emergency situations, and those decisions inevitably reflect ethical choices. Consider the famous “trolley problem” adapted for autonomous vehicles: if a car must choose between swerving into a barrier, likely killing its passenger, or continuing straight and hitting a group of pedestrians, what should it do? How should the algorithm be programmed to weigh the value of different lives? Should it prioritize the vehicle’s occupants or the greater number of people outside the vehicle?
Philosophers and ethicists have debated the trolley problem for decades without reaching consensus, yet autonomous vehicle manufacturers must provide concrete answers embedded in code. Some companies have stated that their vehicles will prioritize avoiding harm to humans outside the vehicle, while others maintain that the vehicle’s first duty is to protect its occupants. There is currently no regulatory guidance on this question, and different companies are making different choices. This lack of standardization means that different autonomous vehicles might make different decisions in identical situations, creating inconsistent and potentially problematic outcomes.
Data privacy represents another significant regulatory challenge. Autonomous vehicles must collect enormous amounts of data to function properly – information about their location, routes, driving patterns, and surroundings. This data is highly valuable both for improving autonomous driving systems and for other commercial purposes. However, it also raises serious privacy concerns. Who owns this data? How long can companies retain it? Can it be sold to third parties? Can law enforcement access it without a warrant? Current data protection laws in most jurisdictions were not written with autonomous vehicles in mind and may be inadequate to address these concerns.
The jurisdictional complexity of autonomous vehicle regulation presents additional complications. In federal systems like the United States, it’s unclear whether regulation should occur at the national or state level. Some states have moved quickly to authorize and regulate autonomous vehicle testing, while others have been more cautious. This patchwork of different state regulations makes it difficult for manufacturers to develop vehicles that can operate legally across the entire country. International differences compound the problem further – a vehicle approved for operation in the United States might not meet European or Asian regulatory standards, potentially fragmenting the global market.
Safety standards themselves are contentious. How safe must an autonomous vehicle be before it’s allowed on public roads? Some argue they should be held to a higher standard than human drivers, allowed only when they can be proven safer than the average human. Others contend that since human drivers cause about 1.3 million deaths globally each year, autonomous vehicles should be deployed as soon as they’re merely as safe as humans, since any delay means preventable deaths continue. Still others worry that rushing deployment before the technology is sufficiently mature could result in accidents that damage public trust and set back the entire field.
Regulators must also consider the infrastructure implications of autonomous vehicles. Should governments invest in “smart roads” with sensors and communication systems that help autonomous vehicles navigate? Or should the vehicles be required to operate on existing infrastructure? How should autonomous vehicles be integrated with traditional human-driven vehicles during the likely decades-long transition period? These questions have massive financial implications for public budgets and could determine the pace and success of autonomous vehicle adoption.
Questions 14-18: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage?
Write:
- YES if the statement agrees with the views of the writer
- NO if the statement contradicts the views of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
-
Traditional traffic laws are adequate for regulating autonomous vehicles.
-
The 2018 Uber accident in Arizona demonstrated how difficult it is to assign responsibility in autonomous vehicle accidents.
-
All insurance companies have agreed to shift the insurance burden to manufacturers.
-
Different autonomous vehicle companies have made different ethical choices about how their vehicles should respond in emergencies.
-
Federal regulation is more effective than state regulation for autonomous vehicles.
Questions 19-23: Matching Headings
The passage has ten paragraphs, A-J.
Choose the correct heading for paragraphs D-H from the list of headings below.
Write the correct number, i-ix.
List of Headings:
i. The challenge of establishing safety thresholds
ii. Commercial value and concerns about personal information
iii. The trolley problem applied to autonomous vehicles
iv. Financial protection models under threat
v. Infrastructure investment decisions
vi. Determining fault in autonomous vehicle accidents
vii. Conflicting regulations across different regions
viii. The role of human monitors in autonomous systems
ix. Insurance premium calculations for traditional vehicles
- Paragraph D: __
- Paragraph E: __
- Paragraph F: __
- Paragraph G: __
- Paragraph H: __
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Autonomous vehicles have created many regulatory challenges for governments. One major problem is determining 24. __ when accidents occur, as it’s unclear whether owners, manufacturers, or software designers should be responsible. The famous 25. __ presents an ethical dilemma about how vehicles should be programmed to respond in emergencies. Additionally, the 26. __ of regulations across different states and countries creates difficulties for manufacturers trying to develop vehicles that can operate everywhere.
PASSAGE 3 – Technical and Systemic Challenges in Autonomous Vehicle Deployment
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The trajectory toward fully autonomous vehicles, while seemingly inexorable given current technological trends and substantial investment, faces formidable obstacles that extend well beyond the purely technical challenges of perception, decision-making, and control systems. A comprehensive analysis reveals that the most intractable problems may lie not in perfecting the technology itself, but in the complex interplay between autonomous systems, existing infrastructure, human behavior, and the broader socio-economic ecosystem into which these vehicles must be integrated.
From a technical standpoint, the fundamental challenge of autonomous vehicle perception remains only partially solved despite remarkable advances in sensor technology and computer vision. While modern AVs can reliably detect and classify objects under optimal conditions, their performance degrades substantially when confronted with what researchers call “edge cases” – unusual or unexpected situations that fall outside the distribution of scenarios encountered during training. The inherent limitation of machine learning systems is that they can only learn from data they have seen or simulations that approximate reality. However, the real world generates an effectively infinite variety of situations, many of which occur so rarely that no training dataset, regardless of size, can adequately represent them.
Adversarial conditions present particularly vexing problems. Heavy snow, fog, or rain can severely impair sensor effectiveness: cameras have difficulty with visibility, LiDAR beams can be scattered by precipitation, and radar, while more weather-resistant, provides less detailed information. Some autonomous vehicle companies have focused their testing primarily in regions with favorable weather conditions, such as Arizona and California, but this strategy merely defers rather than solves the problem. For autonomous vehicles to achieve widespread adoption, they must operate reliably in all weather conditions and geographies, including the snow-covered roads of Minnesota, the dense fog of Seattle, and the torrential rains of the southeastern United States during hurricane season.
The cognitive challenge of general scene understanding also remains elusive. Humans possess contextual understanding and common-sense reasoning that allows them to interpret ambiguous situations and predict the behavior of other road users. An experienced driver seeing children playing near the street will anticipate the possibility that a ball might roll into the road followed by a child chasing it, and will proactively slow down. Current autonomous systems, despite their ability to detect and track objects, lack this anticipatory reasoning capability. They can react to what they observe, but struggle to predict what might happen based on contextual clues. Developing artificial intelligence systems with human-like situational awareness and predictive capability remains an active area of research, but true parity with human cognition may require fundamental breakthroughs in AI rather than merely incremental improvements to existing approaches.
The challenge of ensuring cybersecurity in autonomous vehicles represents another critical dimension that has received insufficient attention relative to its importance. Autonomous vehicles are essentially computers on wheels, connected to external networks for mapping updates, software patches, and potentially vehicle-to-vehicle communication. This connectivity creates vulnerabilities that malicious actors could exploit. A successful cyberattack on an autonomous vehicle could allow hackers to take control of steering, braking, or acceleration, turning the vehicle into a weapon. Scaling this attack to affect multiple vehicles simultaneously could be catastrophic. While cybersecurity measures can mitigate these risks, the history of computing suggests that determined adversaries eventually find ways to compromise even well-protected systems. The consequences of cybersecurity failures in autonomous vehicles could be measured in lives lost, creating an unprecedented safety challenge.
Beyond these technical issues, the economics of autonomous vehicle deployment present substantial barriers to widespread adoption. Developing autonomous driving systems requires enormous investment in research, engineering, sensor hardware, and computing platforms. The sensor suite alone on current autonomous test vehicles can cost between $75,000 and $150,000 – more than most entire conventional vehicles. While costs are expected to decline with mass production, they will likely remain significant for the foreseeable future. For autonomous vehicles to become economically viable for individual ownership, either their cost must decrease dramatically, or their value proposition must justify premium pricing. Alternatively, the economics might favor shared mobility models where the cost is amortized across many riders, but this model faces its own challenges related to fleet management, cleaning, maintenance, and the paradoxical potential to increase rather than decrease overall vehicle miles traveled.
The infrastructure implications of autonomous vehicle deployment represent yet another complex challenge that intertwines technical and policy considerations. While autonomous vehicle advocates often emphasize that AVs should be able to operate on existing roads without infrastructure modifications, there is growing recognition that certain infrastructure augmentations could significantly improve AV performance and safety. High-definition mapping with centimeter-level accuracy, maintained and updated in real-time, could provide autonomous vehicles with crucial information about road conditions, construction zones, and other relevant factors. Vehicle-to-infrastructure (V2I) communication could allow traffic signals to broadcast their status directly to AVs, eliminating the need for unreliable visual recognition of signal states. Dedicated lanes or roads for autonomous vehicles could simplify the operational environment and improve safety during the transition period when autonomous and human-driven vehicles share the roads.
However, implementing such infrastructure improvements would require substantial public investment at a time when many jurisdictions face funding constraints for even basic road maintenance. Furthermore, the question of who should bear these costs remains contentious. Should taxpayers subsidize infrastructure improvements that primarily benefit private autonomous vehicle companies and their customers? Or should autonomous vehicle operators pay fees to support infrastructure development, potentially through tolling mechanisms or special taxes? These questions have no clear answers and touch on fundamental debates about the proper role of government in supporting emerging technologies and the equitable distribution of costs and benefits.
The transition period, during which autonomous and human-driven vehicles share the roads, may prove particularly challenging and could extend for decades. Mixed traffic environments create complications for both types of vehicles. Autonomous vehicles currently struggle to predict and respond to human driver behavior, which can be aggressive, irrational, or inconsistent. Meanwhile, human drivers may not understand how to interact with autonomous vehicles, potentially taking advantage of their cautious behavior or, conversely, being surprised by their movements. Some researchers have suggested that this transition period could actually be less safe than either the current all-human-driver environment or a future all-autonomous environment, creating a “valley of danger” that must be traversed before the full safety benefits of autonomous vehicles can be realized.
Moreover, the assumption that autonomous vehicles will necessarily improve overall societal welfare deserves closer scrutiny. While the potential for reduced accidents is often touted as a primary benefit, other impacts may be less positive. If autonomous vehicles make transportation extremely convenient and affordable, they might induce additional travel, increasing overall vehicle miles traveled, energy consumption, and emissions – particularly if the vehicles are not electric. Autonomous vehicles might also exacerbate urban sprawl if they make long commutes more tolerable by allowing passengers to work or rest during travel. The impact on employment could be profound, with millions of professional drivers – truck drivers, taxi drivers, bus drivers, delivery drivers – potentially losing their livelihoods. While technological unemployment has always been a feature of economic progress, the pace and scale of potential job losses in the transportation sector raises legitimate concerns about social disruption that would need to be managed through policy interventions.
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
-
According to the passage, the main limitation of machine learning systems for autonomous vehicles is that they
A. process information too slowly
B. cannot handle any unusual situations
C. can only learn from previously encountered or simulated scenarios
D. require too much computing power -
The passage suggests that testing autonomous vehicles primarily in favorable weather conditions
A. solves the problem of adverse weather operation
B. is the most practical approach to development
C. only postpones addressing weather-related challenges
D. proves that autonomous vehicles work in all conditions -
What does the author suggest about cybersecurity in autonomous vehicles?
A. It has been completely solved by current technology
B. It represents a potentially life-threatening vulnerability
C. It is not a serious concern for developers
D. It only affects connected vehicles -
According to the passage, high-definition mapping and vehicle-to-infrastructure communication
A. are unnecessary for autonomous vehicle operation
B. have already been implemented in most cities
C. could improve autonomous vehicle performance but require public investment
D. will be funded entirely by autonomous vehicle companies -
The “valley of danger” mentioned in the passage refers to
A. dangerous mountain roads that autonomous vehicles cannot handle
B. the period when autonomous and human-driven vehicles share roads
C. technical vulnerabilities in autonomous vehicle software
D. economic challenges facing the autonomous vehicle industry
Questions 32-36: Matching Features
Match each challenge (32-36) with the correct category (A-F) from the list below.
Write the correct letter, A-F.
Categories:
A. Technical perception challenge
B. Economic barrier
C. Cybersecurity concern
D. Cognitive limitation
E. Infrastructure requirement
F. Social impact concern
Challenges:
-
Autonomous vehicles cannot predict behavior based on contextual clues like human drivers can.
-
The cost of sensor equipment makes autonomous vehicles prohibitively expensive for most consumers.
-
Hackers could potentially take control of multiple vehicles simultaneously.
-
Professional drivers in various industries may lose employment opportunities.
-
Heavy precipitation can scatter LiDAR beams and reduce sensor effectiveness.
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS AND/OR A NUMBER from the passage for each answer.
-
What is the term used for unusual situations that autonomous vehicles rarely encounter during training?
-
What level of mapping accuracy could benefit autonomous vehicles according to the passage?
-
How much can the sensor suite on current autonomous test vehicles cost?
-
Besides accidents reduction, what benefit of autonomous vehicles is mentioned as “often touted”?
Cảm biến xe tự lái hoạt động trong điều kiện thời tiết khắc nghiệt mưa tuyết
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- C
- B
- C
- C
- C
- FALSE
- FALSE
- NOT GIVEN
- FALSE
- machine learning algorithms
- distraction/fatigue
- parking spaces
- all possible scenarios
PASSAGE 2: Questions 14-26
- NO
- YES
- NOT GIVEN
- YES
- NOT GIVEN
- iv
- iii
- ii
- vii
- i
- liability
- trolley problem
- patchwork
PASSAGE 3: Questions 27-40
- C
- C
- B
- C
- B
- D
- B
- C
- F
- A
- edge cases
- centimeter-level (accuracy)
- $75,000 to/and $150,000 (hoặc between $75,000 and $150,000)
- reduced accidents
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: DARPA Grand Challenge, 2004
- Vị trí trong bài: Đoạn 2, dòng 4-6
- Giải thích: Bài đọc nói rõ “While the first challenge in 2004 saw no team complete the course, by 2005, five vehicles successfully finished”. Điều này có nghĩa là năm 2004 không có đội nào hoàn thành khóa học, do đó đáp án C đúng. Đáp án A sai vì 5 đội hoàn thành vào năm 2005, không phải 2004.
Câu 2: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: LiDAR technology, main function
- Vị trí trong bài: Đoạn 3, dòng 2-4
- Giải thích: Bài viết mô tả rõ ràng “LiDAR (Light Detection and Ranging), which creates detailed three-dimensional maps of the vehicle’s surroundings”. Chức năng chính của LiDAR là tạo bản đồ 3D, không phải các chức năng khác được liệt kê trong các đáp án còn lại.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Level 3 autonomous vehicles
- Vị trí trong bài: Đoạn 4, dòng 7-8
- Giải thích: Bài đọc giải thích “Level 3 allows the vehicle to handle all aspects of driving in certain situations, though the driver must be prepared to intervene when requested”. Paraphrase: “intervene when requested” = “take control when asked” trong đáp án C.
Câu 4: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: robotaxi services, cities
- Vị trí trong bài: Đoạn 5, dòng cuối
- Giải thích: Bài viết đề cập cụ thể “Waymo (a subsidiary of Google’s parent company Alphabet), have begun offering limited robotaxi services in selected cities”. Đây là thông tin trực tiếp, không có paraphrase.
Câu 5: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: traffic accidents, caused by
- Vị trí trong bài: Đoạn 6, dòng 2-3
- Giải thích: Bài đọc nói “the vast majority of crashes are caused by human error – factors like distraction, fatigue, impaired driving, and poor judgment”. Human error = human error and judgment trong đáp án C.
Câu 6: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: first DARPA Grand Challenge, several vehicles completing
- Vị trí trong bài: Đoạn 2, dòng 5
- Giải thích: Bài viết nói rõ “the first challenge in 2004 saw no team complete the course” – không có đội nào hoàn thành. Câu phát biểu nói “several vehicles” (một số xe) hoàn thành, điều này mâu thuẫn trực tiếp với thông tin trong bài.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: modern autonomous vehicles, only LiDAR
- Vị trí trong bài: Đoạn 3
- Giải thích: Bài viết liệt kê nhiều công nghệ: LiDAR, radar systems, cameras, GPS systems. Từ “only” trong câu phát biểu làm cho nó sai, vì xe tự lái sử dụng nhiều công nghệ, không chỉ LiDAR.
Câu 8: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Level 2, most common type, consumers
- Vị trí trong bài: Đoạn 5, dòng 1-2
- Giải thích: Bài viết nói “Most vehicles currently on the market with ‘self-driving’ features are actually at Level 2 or Level 3” nhưng không chỉ rõ Level 2 là phổ biến nhất. Nó có thể là Level 2 hoặc Level 3, hoặc cả hai đều phổ biến như nhau.
Câu 9: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: technical challenges, weather conditions, completely eliminated
- Vị trí trong bài: Đoạn 8, dòng cuối
- Giải thích: Bài viết nói rõ “Weather conditions like heavy snow or rain can interfere with sensors”, cho thấy các thách thức về thời tiết vẫn tồn tại, không phải đã được loại bỏ hoàn toàn.
Câu 10: machine learning algorithms
- Dạng câu hỏi: Sentence Completion
- Từ khóa: make quick decisions, controlling the vehicle
- Vị trí trong bài: Đoạn 3, dòng cuối
- Giải thích: Bài viết nói “powerful onboard computers running complex machine learning algorithms that can make split-second decisions”. “Split-second decisions” được paraphrase thành “quick decisions” trong câu hỏi.
Câu 11: distraction/fatigue
- Dạng câu hỏi: Sentence Completion
- Từ khóa: advantage, don’t experience, human drivers
- Vị trí trong bài: Đoạn 6, dòng 4-5
- Giải thích: “Autonomous vehicles don’t get tired, don’t check their phones” – không mệt mỏi (fatigue) và không bị phân tâm (distraction). Cả hai đáp án đều được chấp nhận.
Câu 12: parking spaces
- Dạng câu hỏi: Sentence Completion
- Từ khóa: reduce the need, cities, share
- Vị trí trong bài: Đoạn 7, dòng cuối
- Giải thích: “Some experts even envision a future where private car ownership declines as people rely on fleets of shared autonomous vehicles that come when called, reducing the need for parking spaces in crowded urban areas”.
Câu 13: all possible scenarios
- Dạng câu hỏi: Sentence Completion
- Từ khóa: critics, testing, ensure safety
- Vị trí trong bài: Đoạn 8, dòng cuối
- Giải thích: “critics argue that this is still insufficient to ensure safety in all possible scenarios”. Đây là trích dẫn trực tiếp từ bài.
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Traditional traffic laws, adequate
- Vị trí trong bài: Đoạn 1, dòng 1-4
- Giải thích: Bài viết nói rõ “Traditional traffic laws were written with the assumption that a licensed human driver would be in control… The emergence of vehicles that can operate without human input fundamentally challenges this framework”. Từ “fundamentally challenges” cho thấy tác giả không đồng ý rằng luật lệ truyền thống là đủ (adequate).
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: 2018 Uber accident, difficult to assign responsibility
- Vị trí trong bài: Đoạn 3
- Giải thích: Đoạn văn mô tả vụ tai nạn và nói “the case highlighted the murky nature of responsibility when autonomous systems are involved” và đặt ra nhiều câu hỏi về trách nhiệm. “Murky nature” = “difficult”, cho thấy tác giả đồng ý với quan điểm này.
Câu 16: NOT GIVEN
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: All insurance companies, agreed, shift burden
- Vị trí trong bài: Đoạn 4
- Giải thích: Bài viết nói “Some experts predict” rằng gánh nặng bảo hiểm sẽ chuyển dịch, nhưng không nói rằng tất cả các công ty bảo hiểm đã đồng ý. Đây chỉ là dự đoán của chuyên gia, không phải thực tế.
Câu 17: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Different companies, different ethical choices
- Vị trí trong bài: Đoạn 6, dòng cuối
- Giải thích: “Some companies have stated that their vehicles will prioritize avoiding harm to humans outside the vehicle, while others maintain that the vehicle’s first duty is to protect its occupants… different companies are making different choices”. Đây là bằng chứng rõ ràng.
Câu 18: NOT GIVEN
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Federal regulation, more effective, state regulation
- Vị trí trong bài: Đoạn 8
- Giải thích: Bài viết mô tả sự phức tạp giữa quy định liên bang và bang, nhưng không nói rằng loại nào hiệu quả hơn. Tác giả chỉ trình bày vấn đề mà không đưa ra quan điểm về giải pháp nào tốt hơn.
Câu 19: iv (Paragraph D)
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn D nói về vấn đề bảo hiểm (insurance)
- Giải thích: Đoạn này thảo luận về “Insurance companies face similar dilemmas” và mô hình bảo hiểm ô tô hiện tại “breaks down” với xe tự lái. Heading “Financial protection models under threat” (Các mô hình bảo vệ tài chính bị đe dọa) phù hợp nhất.
Câu 20: iii (Paragraph E)
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn E
- Giải thích: Đoạn này đề cập trực tiếp đến “the famous ‘trolley problem’ adapted for autonomous vehicles” và thảo luận về những lựa chọn đạo đức trong tình huống khẩn cấp.
Câu 21: ii (Paragraph F)
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn F nói về quyền riêng tư dữ liệu (data privacy)
- Giải thích: Đoạn văn thảo luận về “data is highly valuable both for improving autonomous driving systems and for other commercial purposes” và đặt ra các câu hỏi về quyền sở hữu và quyền riêng tư.
Câu 22: vii (Paragraph G)
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn G
- Giải thích: Đoạn này nói về “jurisdictional complexity”, “patchwork of different state regulations”, và “International differences compound the problem” – đều liên quan đến các quy định xung đột khác nhau.
Câu 23: i (Paragraph H)
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn H nói về tiêu chuẩn an toàn (safety standards)
- Giải thích: Đoạn văn đặt câu hỏi “How safe must an autonomous vehicle be before it’s allowed on public roads?” và thảo luận các quan điểm khác nhau về ngưỡng an toàn – đúng với heading về “establishing safety thresholds”.
Câu 24: liability
- Dạng câu hỏi: Summary Completion
- Từ khóa: determining, accidents occur
- Vị trí trong bài: Đoạn 2, câu đầu
- Giải thích: “One of the most contentious issues concerns liability in the event of an accident”. Từ khóa “determining” trong câu hỏi tương ứng với “concerns” trong bài.
Câu 25: trolley problem
- Dạng câu hỏi: Summary Completion
- Từ khóa: ethical dilemma, programmed
- Vị trí trong bài: Đoạn 5
- Giải thích: “Consider the famous ‘trolley problem’ adapted for autonomous vehicles” – đây là tên chính thức của thí nghiệm tư duy đạo đức được đề cập.
Câu 26: patchwork
- Dạng câu hỏi: Summary Completion
- Từ khóa: regulations, different states and countries
- Vị trí trong bài: Đoạn 8, dòng 4
- Giải thích: “This patchwork of different state regulations makes it difficult for manufacturers” – “patchwork” là từ được sử dụng để mô tả sự không thống nhất của các quy định.
Passage 3 – Giải Thích
Câu 27: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main limitation, machine learning systems
- Vị trí trong bài: Đoạn 2, giữa đoạn
- Giải thích: “The inherent limitation of machine learning systems is that they can only learn from data they have seen or simulations that approximate reality”. Đây chính là đáp án C được paraphrase: “previously encountered” = “they have seen”, “simulated scenarios” = “simulations”.
Câu 28: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: testing, favorable weather conditions
- Vị trí trong bài: Đoạn 3, dòng 4-6
- Giải thích: “Some autonomous vehicle companies have focused their testing primarily in regions with favorable weather conditions… but this strategy merely defers rather than solves the problem”. “Defers” = “postpones”, “rather than solves” = không giải quyết vấn đề.
Câu 29: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: cybersecurity, autonomous vehicles
- Vị trí trong bài: Đoạn 5
- Giải thích: “A successful cyberattack on an autonomous vehicle could allow hackers to take control of steering, braking, or acceleration, turning the vehicle into a weapon… The consequences of cybersecurity failures in autonomous vehicles could be measured in lives lost”. Điều này cho thấy đây là lỗ hổng có thể đe dọa mạng sống.
Câu 30: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: high-definition mapping, vehicle-to-infrastructure
- Vị trí trong bài: Đoạn 7
- Giải thích: “High-definition mapping… could provide autonomous vehicles with crucial information” và “Vehicle-to-infrastructure communication could allow traffic signals to broadcast”. Nhưng đoạn 8 nói “implementing such infrastructure improvements would require substantial public investment” – yêu cầu đầu tư công.
Câu 31: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: valley of danger
- Vị trí trong bài: Đoạn 9, cuối đoạn
- Giải thích: “Some researchers have suggested that this transition period could actually be less safe… creating a ‘valley of danger’ that must be traversed”. Transition period là thời kỳ xe tự lái và xe do người lái chia sẻ đường.
Câu 32: D (Cognitive limitation)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 4
- Giải thích: “Current autonomous systems… lack this anticipatory reasoning capability” và “struggle to predict what might happen based on contextual clues” – đây là hạn chế về nhận thức (cognitive limitation).
Câu 33: B (Economic barrier)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 6
- Giải thích: “The sensor suite alone on current autonomous test vehicles can cost between $75,000 and $150,000” – đây rõ ràng là rào cản kinh tế.
Câu 34: C (Cybersecurity concern)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 5
- Giải thích: “Scaling this attack to affect multiple vehicles simultaneously could be catastrophic” – đây là mối lo ngại về an ninh mạng.
Câu 35: F (Social impact concern)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 10, cuối đoạn
- Giải thích: “millions of professional drivers… potentially losing their livelihoods” – đây là tác động xã hội liên quan đến việc làm.
Câu 36: A (Technical perception challenge)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 3
- Giải thích: “LiDAR beams can be scattered by precipitation” – đây là thách thức kỹ thuật về khả năng cảm nhận/nhận biết của xe.
Câu 37: edge cases
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: unusual situations, rarely encounter, training
- Vị trí trong bài: Đoạn 2, dòng 3
- Giải thích: “their performance degrades substantially when confronted with what researchers call ‘edge cases’ – unusual or unexpected situations that fall outside the distribution of scenarios encountered during training”.
Câu 38: centimeter-level (accuracy)
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: mapping accuracy, benefit autonomous vehicles
- Vị trí trong bài: Đoạn 7, dòng 4-5
- Giải thích: “High-definition mapping with centimeter-level accuracy, maintained and updated in real-time, could provide autonomous vehicles with crucial information”.
Câu 39: $75,000 to/and $150,000 hoặc between $75,000 and $150,000
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: sensor suite, current autonomous test vehicles, cost
- Vị trí trong bài: Đoạn 6, dòng 3-4
- Giải thích: “The sensor suite alone on current autonomous test vehicles can cost between $75,000 and $150,000”. Câu hỏi yêu cầu không quá 3 từ và/hoặc số, có thể viết dạng “$75,000 to $150,000” hoặc giữ nguyên “between $75,000 and $150,000”.
Câu 40: reduced accidents
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: benefit, often touted
- Vị trí trong bài: Đoạn 10, dòng 2
- Giải thích: “While the potential for reduced accidents is often touted as a primary benefit”. Từ “touted” trong bài khớp với “touted” trong câu hỏi.
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| autonomous | adj | /ɔːˈtɒnəməs/ | tự động, tự trị | Autonomous vehicles can navigate without human input | autonomous vehicle, autonomous system |
| navigate | v | /ˈnævɪɡeɪt/ | điều hướng, định vị | These vehicles can navigate desert terrain | navigate traffic, navigate safely |
| artificial intelligence | n | /ˌɑːtɪfɪʃl ɪnˈtelɪdʒəns/ | trí tuệ nhân tạo | Using artificial intelligence and complex algorithms | artificial intelligence system |
| sophisticated | adj | /səˈfɪstɪkeɪtɪd/ | tinh vi, phức tạp | Modern AVs are far more sophisticated | sophisticated technology, sophisticated system |
| LiDAR | n | /ˈlaɪdɑːr/ | công nghệ quét laser | Modern AVs use a technology called LiDAR | LiDAR system, LiDAR technology |
| attentive | adj | /əˈtentɪv/ | chăm chú, tập trung | The driver must remain attentive | attentive driver, remain attentive |
| intervene | v | /ˌɪntəˈviːn/ | can thiệp | The driver must be prepared to intervene | intervene when necessary |
| substantial | adj | /səbˈstænʃl/ | đáng kể, lớn | The potential benefits are substantial | substantial benefit, substantial impact |
| distraction | n | /dɪˈstrækʃn/ | sự xao lãng | Accidents caused by distraction | driver distraction, avoid distraction |
| mobility | n | /məʊˈbɪləti/ | khả năng di chuyển | Provide newfound independence and mobility | personal mobility, improved mobility |
| coordination | n | /kəʊˌɔːdɪˈneɪʃn/ | sự phối hợp | This coordination could reduce fuel consumption | traffic coordination, vehicle coordination |
| logged | v | /lɒɡd/ | ghi lại, tích lũy | Autonomous vehicles have logged millions of miles | log data, log miles |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| regulatory vacuum | n phrase | /ˈreɡjələtəri ˈvækjuəm/ | khoảng trống pháp lý | Created a regulatory vacuum | fill a regulatory vacuum |
| liability | n | /ˌlaɪəˈbɪləti/ | trách nhiệm pháp lý | Issues of liability and insurance | legal liability, liability insurance |
| contentious | adj | /kənˈtenʃəs/ | gây tranh cãi | One of the most contentious issues | contentious issue, contentious debate |
| ambiguous | adj | /æmˈbɪɡjuəs/ | mơ hồ, không rõ ràng | The situation becomes considerably more ambiguous | ambiguous situation, ambiguous language |
| negligent | adj | /ˈneɡlɪdʒənt/ | cẩu thả, sơ suất | Charged with negligent homicide | negligent behavior, negligent driver |
| murky | adj | /ˈmɜːki/ | mơ hồ, không rõ ràng | The murky nature of responsibility | murky waters, murky situation |
| dilemma | n | /dɪˈlemə/ | tình thế tiến thoái lưỡng nan | Insurance companies face similar dilemmas | ethical dilemma, moral dilemma |
| grapple with | v phrase | /ˈɡræpl wɪð/ | vật lộn với, đối mặt với | Regulators must grapple with ethical dilemmas | grapple with problems |
| trolley problem | n phrase | /ˈtrɒli ˈprɒbləm/ | bài toán xe đẩy (đạo đức) | The famous trolley problem adapted | ethical trolley problem |
| consensus | n | /kənˈsensəs/ | sự đồng thuận | Without reaching consensus | reach consensus, build consensus |
| standardization | n | /ˌstændədaɪˈzeɪʃn/ | sự tiêu chuẩn hóa | This lack of standardization | lack of standardization |
| jurisdiction | n | /ˌdʒʊərɪsˈdɪkʃn/ | quyền tài phán | Current laws in most jurisdictions | legal jurisdiction, federal jurisdiction |
| patchwork | n | /ˈpætʃwɜːk/ | mảng vá, không thống nhất | This patchwork of different regulations | patchwork of laws |
| deploy | v | /dɪˈplɔɪ/ | triển khai | Should be deployed as soon as possible | deploy technology, deploy vehicles |
| infrastructure | n | /ˈɪnfrəstrʌktʃə(r)/ | cơ sở hạ tầng | The infrastructure implications | transport infrastructure, digital infrastructure |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| trajectory | n | /trəˈdʒektəri/ | quỹ đạo, xu hướng | The trajectory toward fully autonomous vehicles | development trajectory |
| inexorable | adj | /ɪnˈeksərəbl/ | không thể cưỡng lại | Seemingly inexorable given current trends | inexorable trend, inexorable force |
| formidable | adj | /ˈfɔːmɪdəbl/ | ghê gớm, đáng gờm | Faces formidable obstacles | formidable challenge, formidable opponent |
| intractable | adj | /ɪnˈtræktəbl/ | khó giải quyết | The most intractable problems | intractable problem, intractable issue |
| interplay | n | /ˈɪntəpleɪ/ | sự tương tác | The complex interplay between systems | interplay between, complex interplay |
| degrade | v | /dɪˈɡreɪd/ | suy giảm, giảm chất lượng | Their performance degrades substantially | degrade performance, degrade quality |
| edge cases | n phrase | /edʒ keɪsɪz/ | trường hợp đặc biệt/ngoại lệ | Confronted with edge cases | handle edge cases |
| adversarial | adj | /ˌædvəˈseəriəl/ | đối nghịch, bất lợi | Adversarial conditions present problems | adversarial conditions |
| vexing | adj | /ˈveksɪŋ/ | làm phiền, gây khó khăn | Particularly vexing problems | vexing question, vexing issue |
| scattered | v | /ˈskætəd/ | bị phân tán, rải rác | LiDAR beams can be scattered | scattered light, scattered data |
| elusive | adj | /ɪˈluːsɪv/ | khó nắm bắt | General scene understanding remains elusive | elusive goal, elusive concept |
| anticipatory | adj | /ænˈtɪsɪpətəri/ | có tính dự đoán | Lack anticipatory reasoning capability | anticipatory action |
| vulnerability | n | /ˌvʌlnərəˈbɪləti/ | lỗ hổng, điểm yếu | Creates vulnerabilities that hackers could exploit | security vulnerability |
| cyberattack | n | /ˈsaɪbərətæk/ | tấn công mạng | A successful cyberattack on vehicles | launch cyberattack, prevent cyberattack |
| catastrophic | adj | /ˌkætəˈstrɒfɪk/ | thảm khốc | Could be catastrophic | catastrophic failure, catastrophic consequences |
| suite | n | /swiːt/ | bộ (thiết bị) | The sensor suite on test vehicles | sensor suite, software suite |
| viable | adj | /ˈvaɪəbl/ | khả thi | Become economically viable | economically viable, viable solution |
| amortized | v | /ˈæmətaɪzd/ | phân bổ (chi phí) | Cost is amortized across many riders | amortize costs |
| augmentation | n | /ˌɔːɡmenˈteɪʃn/ | sự tăng cường | Infrastructure augmentations could improve performance | infrastructure augmentation |
| contentious | adj | /kənˈtenʃəs/ | gây tranh cãi | Remains contentious | contentious issue |
| equitable | adj | /ˈekwɪtəbl/ | công bằng | Equitable distribution of costs | equitable solution |
| inconsistent | adj | /ˌɪnkənˈsɪstənt/ | không nhất quán | Human behavior can be inconsistent | inconsistent behavior |
| touted | v | /taʊtɪd/ | được quảng cáo, ca ngợi | Often touted as a primary benefit | widely touted |
| induce | v | /ɪnˈdjuːs/ | gây ra, dẫn đến | Might induce additional travel | induce change, induce behavior |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng hơn | Might exacerbate urban sprawl | exacerbate problems |
| profound | adj | /prəˈfaʊnd/ | sâu sắc, nghiêm trọng | The impact could be profound | profound impact, profound effect |
| disruption | n | /dɪsˈrʌpʃn/ | sự gián đoạn, đảo lộn | Concerns about social disruption | social disruption, market disruption |
Thách thức pháp lý đạo đức trong quy định quản lý xe tự lái
Kết Bài
Chủ đề về những thách thức trong việc quản lý xe tự lái đại diện cho một lĩnh vực quan trọng trong IELTS Reading hiện đại, phản ánh sự giao thoa giữa công nghệ, pháp luật và xã hội. Qua đề thi mẫu này, bạn đã được trải nghiệm một bài thi hoàn chỉnh với ba passages có độ khó tăng dần, từ giới thiệu cơ bản về công nghệ xe tự lái cho đến các vấn đề phức tạp về quy định pháp lý và những thách thức kỹ thuật sâu sắc.
Ba passages trong đề thi này đã cung cấp đầy đủ phổ độ khó từ Easy (Band 5.0-6.5) đến Medium (Band 6.0-7.5) và Hard (Band 7.0-9.0), giúp bạn làm quen với cách IELTS đánh giá khả năng đọc hiểu của thí sinh ở các trình độ khác nhau. Với 40 câu hỏi bao gồm 8 dạng câu hỏi phổ biến, bạn đã có cơ hội luyện tập toàn diện các kỹ năng cần thiết cho bài thi thực tế.
Phần đáp án chi tiết không chỉ cung cấp đáp án đúng mà còn giải thích rõ ràng vị trí thông tin trong bài, cách paraphrase được sử dụng, và lý do tại sao các đáp án khác không chính xác. Điều này giúp bạn hiểu sâu hơn về cách IELTS kiểm tra khả năng đọc hiểu và phát triển kỹ năng phân tích câu hỏi hiệu quả.
Hệ thống từ vựng được phân loại theo từng passage, cùng với phiên âm, nghĩa tiếng Việt, ví dụ thực tế và collocations, sẽ giúp bạn xây dựng vốn từ vựng học thuật cần thiết không chỉ cho IELTS Reading mà còn cho toàn bộ kỳ thi IELTS. Hãy dành thời gian học và ôn tập những từ vựng này thường xuyên để nâng cao band điểm của bạn.
Để đạt kết quả tốt nhất, hãy làm lại đề thi này nhiều lần, mỗi lần tập trung vào một kỹ năng khác nhau: lần đầu làm trong điều kiện thi thật với giới hạn thời gian, lần sau phân tích kỹ các câu hỏi và đoạn văn, lần tiếp theo tập trung vào việc paraphrase và tìm từ đồng nghĩa. Sự kiên trì và phương pháp luyện tập đúng đắn sẽ giúp bạn tự tin bước vào phòng thi IELTS và đạt được band điểm mong muốn.
