Criticality Assessment of Simulation-Based AV/ADAS Test Scenarios Ohio State University

Format:

Book

Conference/Event

Author/Creator:

Tulpule, Punit, author.

Contributor:

Chen, Bo-Shian

Vaidya, Umesh

Conference Name:

WCX SAE World Congress Experience (2022-04-05 : Detroit & Online, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2022

Summary:

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, et cetera 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

Notes:

Vendor supplied data

Publisher Number:

2022-01-0070

Access Restriction:

Restricted for use by site license