Definition
Autonomous Vehicle (AV) Safety is the engineering and ethical discipline of making self-driving systems reliable enough for public roads. It combines Computer Vision, sensor fusion, and Reasoning to navigate a complex and unpredictable world.
Key Safety Technologies
1. Sensor Redundancy
AVs use multiple types of sensors so they aren't blinded by a single failure:
- LiDAR: Uses lasers to create a 3D map of the surroundings.
- Radar: Excels at measuring the speed of other cars, even in fog or rain.
- Cameras: Crucial for reading traffic lights and stop signs using Deep Learning.
2. Sensor Fusion
The process of combining data from all sensors into a single, coherent "world model." If the camera sees a shadow but the LiDAR sees a solid object, the safety system must correctly decide what is real.
3. Redundant Systems
Secondary computers, brakes, and steering systems that can take over instantly if the primary system fails.
Measuring Safety
- Disengagements: When a human driver has to take control of an autonomous car.
- Miles per Crash: Comparing the mileage between accidents to that of human drivers.
- Simulation Testing: Driving billions of virtual miles to test how the AI handles rare "edge cases" without risk to life.
Real-World Implementations
Companies like Waymo have logged over 100 million autonomous miles, providing a massive dataset used to prove their safety case to regulators. Their expansion into cities like London (as seen in our blog post) represents a major milestone in global AV deployment.
Future Outlook
As AI Safety techniques improve, we will see better coordination between vehicles (V2V communication) and a move toward standardized global safety regulations.