动力学(音乐)
人工神经网络
计算机科学
统计物理学
物理
航空航天工程
人工智能
工程类
声学
作者
John Chrosniak,Jingyun Ning,Madhur Behl
出处
期刊:IEEE robotics and automation letters
日期:2024-04-15
卷期号:9 (6): 5292-5297
被引量:1
标识
DOI:10.1109/lra.2024.3388847
摘要
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 ( $>$ 280 km/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 autonomous racecar vehicle dynamics modeling. It merges physics coefficient estimation and dynamical equations to accurately predict vehicle states at high speeds. A unique Physics Guard layer ensures 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.
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