Autonomous Driving Safety Evaluation

The field assesses whether automated vehicles can avoid collisions, follow traffic rules, handle uncertainty, and recover from failures across interactions with people, vehicles, and road infrastructure. Common approaches include closed-loop simulation and road tests, safety-critical scenario generation, risk and near-miss metrics, formal rule checking, and fault or sensor-perturbation testing. Benchmarks increasingly probe long-tail events, vulnerable road users, domain shifts, and the gap between open-loop prediction scores and real driving behavior. A central challenge is making tests both realistic and broad enough to reveal failures, while ensuring that improvements in simulation or benchmark scores transfer to deployed vehicles.