Mandatory lane-changing decision and control method based on game theory.
DOI: 10.1371/journal.pone.0350209
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Summary
This paper addresses the challenge of mandatory lane-changing for autonomous vehicles, particularly in the vicinity of signalized intersections where spatial constraints and traffic interactions create high safety risks. Existing methods often treat surrounding vehicles as passive obstacles, failing to capture the dynamic, interactive nature of traffic flow. To resolve this, the authors propose a unified framework that integrates game-theoretic decision-making with robust trajectory tracking control, aiming to balance safety, efficiency, and comfort while accounting for real-time feedback from neighboring vehicles. The methodology models the lane-changing process as a multi-stage Stackelberg leader-follower game. The autonomous vehicle (leader) interacts with a following vehicle in the target lane (follower), with strategy sets defined for lane change, drive straight, or rollback for the leader, and acceleration or deceleration for the follower. Payoff functions are constructed to quantify trade-offs among safety, efficiency, and comfort, incorporating variables such as lateral distance, headway, and driving style coefficients that adjust based on proximity to lane-change prohibition zones. For trajectory execution, the authors design a preset performance-based sliding mode controller. This controller defines a composite error combining lateral displacement and heading angle deviations, constraining these errors within a prescribed performance boundary to ensure transient safety. The constrained error dynamics are transformed into an unconstrained form to guarantee robust tracking. The study evaluates the proposed framework using a joint simulation platform that integrates traffic simulation with driver-in-the-loop experiments. A preliminary study involving three human drivers with varying driving tendencies was conducted to analyze interaction behaviors in mandatory lane-changing scenarios. The results demonstrate that the framework effectively adapts driving strategies and planned trajectories based on surrounding vehicle interactions. The game-theoretic approach allows the autonomous vehicle to dynamically update decisions, such as suspending maneuvers when facing aggressive following vehicles or retreating to the lane centerline when lateral distances fall below safety thresholds. The sliding mode controller ensures that tracking errors remain within safety-critical limits throughout the maneuver, providing improved transient response compared to conventional control strategies. The significance of this work lies in its integration of interaction-aware decision-making with safety-critical trajectory control. By modeling lane changing as a dynamic game rather than a static decision, the method enhances the robustness and interpretability of autonomous driving systems in complex environments. The explicit constraint on transient tracking performance addresses a critical gap in existing control methods, which often prioritize steady-state accuracy over immediate safety during highly interactive maneuvers. This approach offers a more reliable solution for mandatory lane-changing scenarios, contributing to improved traffic efficiency and safety in future transportation systems.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | PubMed Central | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | openalex | — | — | 1 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Theoretical Contribution: computational model