[2512.24075] Multi-Scenario Highway Lane-Change Intention Prediction: A Temporal Physics-Informed Multi-Modal Framework

Computer Science > Machine Learning arXiv:2512.24075 (cs) [Submitted on 30 Dec 2025 (v1), last revised 3 Mar 2026 (this version, v3)] Title:Multi-Scenario Highway Lane-Change Intention Prediction: A Temporal Physics-Informed Multi-Modal Framework Authors:Jiazhao Shi, Ziyu Wang, Yichen Lin, Shoufeng Lu View a PDF of the paper titled Multi-Scenario Highway Lane-Change Intention Prediction: A Temporal Physics-Informed Multi-Modal Framework, by Jiazhao Shi and 3 other authors View PDF Abstract:Lane-change intention prediction is safety-critical for autonomous driving and ADAS, but remains difficult in naturalistic traffic due to noisy kinematics, severe class imbalance, and limited generalization across heterogeneous highway scenarios. We propose Temporal Physics-Informed AI (TPI-AI), a hybrid framework that fuses deep temporal representations with physics-inspired interaction cues. A two-layer bidirectional LSTM (Bi-LSTM) encoder learns compact embeddings from multi-step trajectory histories; we concatenate these embeddings with kinematics-, safety-, and interaction-aware features (e.g., headway, TTC, and safe-gap indicators) and train a LightGBM classifier for three-class intention recognition (No-LC, Left-LC, Right-LC). To improve minority-class reliability, we apply imbalance-aware optimization including resampling/weighting and fold-wise threshold calibration. Experiments on two large-scale drone-based datasets, highD (straight highways) and exiD (ramp-rich environments),...