Abstract

The long-term coexistence of human-driven vehicles (HVs) and autonomous vehicles (AVs) in mixed traffic presents significant challenges for lane-change interactions on freeways. To address this, we propose a closed-loop decision-making framework, centered on Social Value Orientation (SVO), that covers the entire process from recognition to fallback execution. First, we use maximum-entropy inverse reinforcement learning (MaxEnt-IRL) to infer driver SVO parameters (θSVO) from the NGSIM dataset, quantifying the trade-off between selfish and cooperative behaviors as learnable weights. These parameters are then incorporated into a Transformer-based predictor via conditional embeddings, enabling the model to generate personalized trajectories from identical historical data. Furthermore, within a receding-horizon, game-theoretic framework, we combine preference-weighted payoffs with this conditional predictor and introduce a dynamic lane-change abort mechanism. This mechanism triggers a fallback maneuver, generated by an APF + MPC controller, if the expected return of continuing the lane change drops below that of aborting. Simulations across 1000 adversarial scenarios show that our method markedly improves the lane-change success rate and cruising efficiency compared to the IDM + MOBIL baseline. It also significantly reduces forced merges and hazardous events when encountering aggressive or selfish blocking vehicles, demonstrating the safety and robustness benefits of our preference-aware model and abort mechanism.

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