In the early hours of the 19th, local time, a self-driving Uber vehicle in Arizona struck and killed a woman crossing the road. This incident marked the first fatality involving an autonomous vehicle on a public road, pushing Uber to the edge of its reputation.
A few hours later, the director of the Tempe Police Department, responsible for investigating the accident, stated that preliminary findings suggested Uber might not be at fault. The sudden shift in narrative left many confused.
From the crash scene and the configuration of the Uber car, it appeared that the radar and camera systems failed to perform as expected. To uncover the truth, the Tempe Police released video footage from the accident. According to the video, the Uber autonomous vehicle (a Volvo XC90) was speeding through a dark environment with only headlights providing illumination. The victim suddenly appeared in front of the car, which was moving at high speed and hit her instantly. Neither the human monitor nor the self-driving system responded in time.
However, if no one reacted, why didn’t the car? The introduction of self-driving technology raises the question: is it simply meant to free drivers’ hands, or does it require the same level of driving skill as a human? The answer is clearly no.
So, what caused Uber’s self-driving car to fail? To understand this, it’s necessary to look into how the car was designed to operate. According to the crash scene, the accident vehicle was developed by Uber’s Advanced Technology Group (ATG). Uber had previously claimed the car was equipped with a comprehensive sensor system, including lidar on top, radar in front and behind, and optical cameras for real-time imaging. Despite this, the accident still occurred.
The most likely cause is that the sensors were either underperforming or their integration was flawed. In fact, the poor coordination between the lidar, radar, and camera systems may have led to the accident. For self-driving cars to be safe, it's crucial to understand each sensor’s strengths and weaknesses.
Mark Rosenk, former chairman of the U.S. National Transportation Safety Board, noted that this accident could hinder the future development of autonomous vehicles. Solving these issues is essential to regain public trust. Radar and camera systems are among the top priorities.
There are three main types of radar used in autonomous driving: lidar, ultrasonic radar, and millimeter-wave radar. Lidar uses lasers to create 3D images around the vehicle and offers high accuracy and wide detection range. However, it can be affected by weather conditions like rain and snow and is expensive. Ultrasonic radar detects obstacles using sound waves and is cost-effective, often used for parking assistance. Millimeter-wave radar works well in various weather conditions and is widely used due to its affordability.
Cameras play a vital role in capturing visual data, processing images, and identifying objects. They are commonly used for lane detection, pedestrian recognition, and collision warning. While effective, they struggle in low-light environments.
Tesla relies heavily on cameras, while Baidu uses a combination of radar and cameras. Each approach has its pros and cons. Tesla’s system is cost-effective but lacks long-range detection capabilities. Baidu’s system includes lidar, offering better performance in low light but at a higher cost.
Uber’s setup included lidar, radar, and cameras, but the integration may have been flawed. The challenge lies in choosing the right sensor combination based on the application scenario. Highways require long-range detection, while urban areas demand precise object recognition.
In conclusion, the future of autonomous driving depends on balancing sensor performance, cost, and environmental adaptability. As the industry evolves, continuous improvement in hardware and software will be key to ensuring safety and public confidence.
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