Deep Dynamics: Vehicle Dynamics Modeling with a Physics-Constrained Neural Network for Autonomous Racing

Abstract

Autonomous racing is a critical research area for autonomous driving, presenting significant challenges in vehicle dynamics modeling, such as balancing model precision and computational efficiency at high speeds (>280km/h), where minor errors in modeling have severe consequences. Existing physics-based models for vehicle dynamics require elaborate testing setups and tuning, which are hard to implement, time-intensive, and cost-prohibitive. Conversely, purely data-driven approaches do not generalize well and cannot adequately ensure physical constraints on predictions. This paper introduces Deep Dynamics, a physics-constrained neural network (PCNN) for vehicle dynamics modeling of an autonomous racecar. It combines physics coefficient estimation and dynamical equations to accurately predict vehicle states at high speeds and includes a unique Physics Guard layer to ensure internal coefficient estimates remain within their nominal physical ranges. Open-loop and closed-loop performance assessments, using a physics-based simulator and full-scale autonomous Indy racecar data, highlight Deep Dynamics as a promising approach for modeling racecar vehicle dynamics.

Published in the IEEE Robotics and Automation Letters and presented at the IEEE International Conference on Intelligent Robots and Systems

References in corpus (2)

• F1/10: An Open-Source Autonomous Cyber-Physical Platform
• RACECAR -- The Dataset for High-Speed Autonomous Racing

Cited by in corpus (3)

• Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute
• Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation
• VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics