Scenario Taxonomy
6 categories, 27 sub-scenarios classifying all known L4+ robotaxi operational anomalies. Each maps to a recommended response layer (1/2/3).
Layer 1: AI Autonomous Response (70%)
Layer 2: AI-Assisted + Human Confirm (25%)
Layer 3: Remote Driving / On-Site (5%)
A
System-Wide Failure
系统性故障
Anomalies affecting multiple vehicles simultaneously due to infrastructure, cloud, or platform-level failures.
B
Perception/Decision Failure
感知/决策失效
Vehicle's perception system or decision-making logic fails to correctly interpret the driving environment.
C
Planning/Execution Anomaly
规划/执行异常
Vehicle's planning or control system produces dangerous or frozen output despite correct perception.
D
Vehicle Hardware Failure
车辆硬件故障
Anomalies caused by failure of vehicle hardware components — sensors, powertrain, battery, or actuators.
E
External Conflict
外部环境冲突
Anomalies caused by external events or road users that the ADS must respond to.
F
Passenger-Side Issue
乘客端异常
Anomalies originating from or primarily affecting passengers.