Criticality Assessment of Simulation-Based AV/ADAS Test Scenarios
- Delivery
- Provide download link
- Format
- Price
- Non-members (tax incl.)：￥6,600 Members (tax incl.)：￥5,280
- Paper/Info type
- SAE Paper
No.2022-01-0070
- Pages
- 1-7(Total 7 p)
- Date of publication
- Mar 2022
- Publisher
- SAE International
- Language
- English
- Event
- WCX SAE World Congress Experience 2022
Detailed Information
| Author(E) | 1) Punit Tulpule, 2) Bo-Shian Chen, 3) Umesh Vaidya |
|---|---|
| Affiliation(E) | 1) Ohio State University, 2) Ohio State University, 3) Clemson University |
| Abstract(E) | Testing any new safety technology of Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) requires simulation-based validation and verification. The specific scenarios used for testing, outline incidences of accidents or near-miss events. In order to simulate these scenarios, specific values for all the above parameters are required including the ego vehicle model. The ‘criticality’ of a scenario is defined in terms of the difficulty level of the safety maneuver. A scenario could be over-critical, critical, or under-critical. In over-critical scenarios, it is impossible to avoid a crash whereas, for under-critical scenarios, no action may be required to avoid a crash. The criticality of the scenario depends on various parameters e.g. speeds, distances, road/tire parameters, etc. In this paper, we propose a definition of criticality metric and identify the parameters such that a scenario becomes critical. The criticality of a scenario should be independent of the controller or the driver model. Hence, we use an optimal control as the ‘best’ candidate. The proposed approach has three key steps - 1) obtain optimal control for given dynamic and static constraints, 2) compute the probability of a crash assuming small variations in model parameters and control action, and 3) compute occupancy metric over the criticality parameters design space. The occupancy metric, which is related to the value function of the optimal control, defines the criticality of the scenario. The key benefit of this approach is a clear definition of criticality metric which reflects the probability of collision. The proposed approach is demonstrated using an example of an obstacle avoidance maneuver. |
