A Simulation Tool for Virtual Validation and Verification of Advanced Driver Assistance Systems Ohio State University

Format:

Book

Conference/Event

Author/Creator:

Tulpule, Punit, author.

Contributor:

Jin, Yishen

Karumanchi, Aditya

Midlam-Mohler, Shawn

Conference Name:

SAE WCX Digital Summit (2021-04-13 : Live Online, Pennsylvania, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

Due to the infeasibility of exhaustive on-road testing of Automated Vehicles (AVs) and vehicles with Advanced Driver Assistance Systems (ADAS), virtual methods for verification and validation of such vehicles have gained prominence. In order to incorporate the variability in the characteristics of test scenarios such as surrounding traffic, weather, obstacles, road network, infrastructure features, et cetera, as well as provide the option of varying the fidelities of subsystem models, this study discusses a modular software block-set for virtual testing of AV/ADAS controllers based on open source tools. The core concept is to co-simulate the traffic, vehicle dynamics, sensors, and the 3D scenes required for perception. This is achieved using SUMO (Simulation of Urban MObility, a microscopic road-network-based traffic generation tool) and Unreal Engine (for 3D traffic flow generation). Due to the difference in the simulation timestep sizes of SUMO and Unreal Engine, as well as some of the subsystems such as vehicle sensors, such simulations pose a time-synchronization problem. Since SUMO also does not model detailed vehicle dynamics, a vehicle dynamics filter is utilized for multi-timestep simulation. In this paper, we present the architecture of the block-set, the uniqueness of which lies in its modularity in terms of mixing and matching various models for the subsystems involved. An example case using an existing environment in Unreal Engine is demonstrated. This block-set will allow the validation and verification of ADAS controls and perception algorithms in large numbers of scenarios as well as at edge cases which are particularly safety-critical. The simulations can be run in parallel, and specific test cases can also be visualized and individually analyzed

Notes:

Vendor supplied data

Publisher Number:

2021-01-0865

Access Restriction:

Restricted for use by site license