How does an advanced driver assistance system get from raw sensor data to a brake command in about 100 milliseconds, and which stage of that chain actually breaks? The position here is that object classification gets the attention while sensor fusion and training data carry the real risk. A system can label a scene beautifully and still arbitrate a camera and radar conflict badly, which is how a car reads a white truck against a bright sky as open road.
You come away able to describe each stage of the pipeline, explain why cameras, radar and lidar are blind in different ways, read vendor accuracy claims such as OpenAI's 99 percent benchmark figure against independent coverage from MIT Technology Review and O'Reilly Radar, and press vendors on failure behaviour instead of accuracy scores.
0:00 How ADAS decides to brake before you do
0:47 Cameras, radar and lidar tradeoffs
1:11 Sensor fusion and bad arbitration crashes
2:06 Object classification and the edge case problem
3:35 Decision logic and the 100 millisecond budget
4:18 What to ask ADAS vendors for
