Developing accurate models for the behavior of heavy-duty vehicles such as trucks and buses is crucial for ensuring their safe navigation. It is important that these models accurately reflect the vehicle's performance across different weather conditions, road types, and cargo loads. This paper presents the use of Physics-Informed Long Short-Term Memory (PI-LSTM) networks as dynamic models tailored for sampling-based motion planning, which is relevant for the navigation of autonomous vehicles. By combining the LSTM's capability to predict nonlinear vehicle dynamics with physics-based constraints incorporated into the loss function, our planner will be able to generate motion plans that are not only safer due to improved dynamic accuracy but also efficient and capable of full parallel execution on a GPU to significantly enhance planning speed. Evaluating our model using real data from vehicle tests on different road surfaces and driving maneuvers, we see that PI-LSTMs capture vehicle behavior with a significantly lower error than traditional modelling techniques.
Motion Planning Using Physics-Informed LSTMs for Autonomous Driving
2024-09-24
1958314 byte
Conference paper
Electronic Resource
English
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