First of all, one of the challenges we face today is to interpret sensor information. From the police’s published video, it can be seen that even if the ability to detect and classify objects is the most basic building block in a self-driving car system, it is actually a very challenging task. However, this capability is at the heart of the current Advanced Driver Assistance System (ADAS), which includes features such as automatic emergency braking (AEB) and lane keeping. Billions of miles of mileage are enough to verify that the high-accuracy sensing system in ADAS is saving lives. As such, this technology is also an essential element for the future of fully-automated driving of cars before overcoming even greater challenges.
To prove the capabilities and subtleties of the current ADAS technology, we run Mobileye's software on the TV monitor video, which broadcasts police accident footage. In spite of the poor conditions - which may have caused the loss of many high-dynamic-range data at the scene, Mobileye's software was clearly detected about one second before the impact. The bottom image shows three snapshots of the bike bounding box detected above and Ms. Herzberg. The test has two independent sources of operation: pattern recognition (making the bounding box) and a "free space" detection module (producing a horizontal map where the red part indicates a "passerby" above the red line). Using motion structures (technical term “plane + parallaxâ€), the third module can distinguish roads and objects. This verifies that the detected object is 3D, but is less reliable and is therefore described as "fcvValid: Low" and is displayed at the top left of the screen. The low confidence level is due to the lack of information normally available for production vehicles and poor image quality. After all, there may be some unknown downsampling when shooting again based on the video taken by the driving recorder.
The image comes from a video clip on the TV monitor released by the police. The overlay image shows the response of the Mobileye ADAS system. The green and white border boxes are the output of the bicycle and pedestrian detection modules. The horizontal map shows the boundary between roads and obstacles, which we call "free space."
The software used in this experiment is the same as the software used in vehicles currently equipped with ADAS, and has been validated on billions of miles of user miles.
Now, the development of artificial intelligence technologies such as deep neural networks has led many people to believe that high-precision object detection systems can be developed with ease, and those computer vision experts with more than ten years of experience are considered to be greatly reduced. This makes many newcomers flood into this area. While these new technologies are indeed useful, many traditions cannot be ignored. This includes identifying and completing hundreds of extreme test cases, annotating tens of millions of data sets, and challenging on dozens of ADAS projects. Trial production verification test. Experience is crucial, especially in areas where safety is at the forefront.
The second observation of the incident is transparency. Everyone said "we put safety in the most important position", but we believe that in order to gain public trust, we must be more transparent. As I said in Mobileye's release of the Responsible Sensitive Security Model (RSS) in October last year, decisions must meet the common sense of human judgment. We formulate mathematical concepts such as "dangerous situations" and "correct responses" and formulate a system that mathematically ensures compliance with the definitions.
The third observation is redundancy. True sensory systems require redundant designs and must rely on independent sources of information: cameras, radars, and lidars. Consolidating these sources of information helps improve driving comfort, but it is not conducive to safety. To demonstrate that we have achieved practical redundancy, Mobileye has developed an independent, camera-only end-to-end system as well as an independent lidar and radar-only system.
If similar accidents occur again last week, the already fragile trust of users will be further reduced, and passive supervision will be triggered, eventually killing this important job. As I said when I introduced the Responsible Sensitive Security Model, I firmly believe that there is now a need to make a meaningful discussion on the security verification framework for fully automated driving cars. We invite automotive manufacturers, technology companies in the field, regulatory agencies, and other interested parties to work together to resolve these important issues.
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