Departmental Papers (CIS)

Date of this Version

4-2020

Document Type

Conference Paper

Comments

23rd ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2020), Sydney, Australia, April 21-24, 2020

Abstract

This paper describes a verification case study on an autonomous racing car with a neural network (NN) controller. Although several verification approaches have been recently proposed, they have only been evaluated on low-dimensional systems or systems with constrained environments. To explore the limits of existing approaches, we present a challenging benchmark in which the NN takes raw LiDAR measurements as input and outputs steering for the car. We train a dozen NNs using reinforcement learning (RL) and show that the state of the art in verification can handle systems with around 40 LiDAR rays. Furthermore, we perform real experiments to investigate the benefits and limitations of verification with respect to the sim2real gap, i.e., the difference between a system’s modeled and real performance. We identify cases, similar to the modeled environment, in which verification is strongly correlated with safe behavior. Finally, we illustrate LiDAR fault patterns that can be used to develop robust and safe RL algorithms.

Subject Area

CPS Safe Autonomy

Publication Source

23rd ACM International Conference on Hybrid Systems: Computation and Control (HSCC 2020)

DOI

doi.org/10.1145/3365365.3382216

Keywords

Neural Network Verification, Learning for Control, F1/10 Racing

Share

COinS
 

Date Posted:05 March 2020

This document has been peer reviewed.