Diagnosing and Predicting Autonomous Vehicle Operational Safety Using Multiple Simulation Modalities and a Virtual Environment

TL;DR

This paper presents a hybrid simulation test bed for autonomous vehicles that combines vehicle-in-the-loop and model-in-the-loop approaches, enabling cost-effective safety diagnosis and prediction of vehicle responses under various virtual environmental conditions.

Contribution

The work introduces a novel AV simulation setup that integrates VIL and MIL simulations for improved safety analysis and edge-case discovery without extensive real-world testing.

Findings

Demonstrated response diagnosis linking virtual environment to vehicle behavior.

Showed response prediction capabilities without physical vehicle use.

Validated the setup's effectiveness for safety-critical scenario analysis.

Abstract

Even as technology and performance gains are made in the sphere of automated driving, safety concerns remain. Vehicle simulation has long been seen as a tool to overcome the cost associated with a massive amount of on-road testing for development and discovery of safety critical "edge-cases". However, purely software-based vehicle models may leave a large realism gap between their real-world counterparts in terms of dynamic response, and highly realistic vehicle-in-the-loop (VIL) simulations that encapsulate a virtual world around a physical vehicle may still be quite expensive to produce and similarly time intensive as on-road testing. In this work, we demonstrate an AV simulation test bed that combines the realism of vehicle-in-the-loop (VIL) simulation with the ease of implementation of model-in-the-loop (MIL) simulation. The setup demonstrated in this work allows for response diagnosis for the VIL simulations. By observing causal links between virtual weather and lighting conditions that surround the virtual depiction of our vehicle, the vision-based perception model and controller of Openpilot, and the dynamic response of our physical vehicle under test, we can draw conclusions regarding how the perceived environment contributed to vehicle response. Conversely, we also demonstrate response prediction for the MIL setup, where the need for a physical vehicle is not required to draw richer conclusions around the impact of environmental conditions on AV performance than could be obtained with VIL simulation alone. These combine for a simulation setup with accurate real-world implications for edge-case discovery that is both cost effective and time efficient to implement.