Physics-Informed Neural Network-Based Open Set Classification of Neighboring Vehicle Motion for Decision-Making in Autonomous Driving

In this paper, a method for inferring the motion intentions of a neighboring vehicle ahead of an ego vehicle using a physics-informed deep neural network-based open-set classification approach is proposed. Relative motion data from real-world driving were categorized into known and unknown scenarios, with key feature trajectories represented as spatiotemporal 3D input data. A convolutional long short-term memory architecture was designed, and a novel loss function was proposed to incorporate physics-informed perspective and constraints, with integrated loss function’s convergence demonstrated for training. The proposed method was evaluated against comparative five classifiers in terms of 1) classification accuracy for known classes in single scenarios, 2) open-set classification accuracy, 3) analysis by deep reduced feature visualization, 4) generalization performance to unknown data, and 5) classification robustness and in-path decision validity in continuous scenarios. Results showed a 23.5% average improvement in accuracy, the highest generalization performance, and superior robustness, enabling faster and more reliable in-path detection compared to conventional radar including in ambiguous situations.