Safe and Efficient Lane-Changing for Autonomous Vehicles: An Improved Double Quintic Polynomial Approach with Time-to-Collision Evaluation

2025-08-30

AI Summary

To address safety and comfort challenges in autonomous vehicle lane-changing maneuvers amid mixed traffic with human drivers, this paper proposes an enhanced double-quintic polynomial trajectory planning method integrated with time-to-collision (TTC) evaluation. The core contribution is the first incorporation of an analytical TTC penalty term directly into a closed-form double-quintic polynomial solver, enabling real-time, safety-aware trajectory generation without post-hoc validation—thereby bridging the gap between model-driven and adaptive planning. The method jointly incorporates state estimation, real-time TTC computation, and adaptive trajectory assessment to ensure dynamic collision avoidance and motion smoothness. Simulation results demonstrate that the proposed approach significantly outperforms conventional quintic polynomials, Bézier curves, and B-splines in terms of safety, ride comfort, and lane-changing efficiency.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Autonomous driving technology has made significant advancements in recent years, yet challenges remain in ensuring safe and comfortable interactions with human-driven vehicles (HDVs), particularly during lane-changing maneuvers. This paper proposes an improved double quintic polynomial approach for safe and efficient lane-changing in mixed traffic environments. The proposed method integrates a time-to-collision (TTC) based evaluation mechanism directly into the trajectory optimization process, ensuring that the ego vehicle proactively maintains a safe gap from surrounding HDVs throughout the maneuver. The framework comprises state estimation for both the autonomous vehicle (AV) and HDVs, trajectory generation using double quintic polynomials, real-time TTC computation, and adaptive trajectory evaluation. To the best of our knowledge, this is the first work to embed an analytic TTC penalty directly into the closed-form double-quintic polynomial solver, enabling real-time safety-aware trajectory generation without post-hoc validation. Extensive simulations conducted under diverse traffic scenarios demonstrate the safety, efficiency, and comfort of the proposed approach compared to conventional methods such as quintic polynomials, Bezier curves, and B-splines. The results highlight that the improved method not only avoids collisions but also ensures smooth transitions and adaptive decision-making in dynamic environments. This work bridges the gap between model-based and adaptive trajectory planning approaches, offering a stable solution for real-world autonomous driving applications.

Problem

• Ensuring safe lane-changing with human-driven vehicles
• Integrating real-time collision evaluation into trajectory optimization
• Generating smooth adaptive trajectories in dynamic traffic

Innovation

• Double quintic polynomial trajectory generation
• Real-time TTC embedded optimization
• Closed-form safety-aware solver