Safe Autonomous Lane Changing: Planning with Dynamic Risk Fields and Time-Varying Convex Space Generation

2025-11-27

AI Summary

To jointly optimize safety, efficiency, and ride comfort for autonomous lane-changing in dynamic interactive traffic scenarios, this paper proposes a trajectory planning framework integrating a differentiable dynamic risk field with a time-varying convex feasible space. The method introduces a novel differentiable dynamic risk field model that unifies risk perception and kinematic constraints; couples it with a time-varying convex feasible space generation strategy to ensure collision avoidance and trajectory feasibility; and employs constrained iterative Linear Quadratic Regulator (iLQR) to jointly optimize trajectory smoothness, control effort, and risk exposure over a finite horizon. Simulation results demonstrate that the proposed approach achieves a lane-change distance of 28.59 m in 2.84 s, outperforming Artificial Potential Field (APF), Model Predictive Control (MPC), and Rapidly-exploring Random Tree (RRT) methods in safety margin, acceleration comfort, and curvature smoothness.

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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This paper presents a novel trajectory planning pipeline for complex driving scenarios like autonomous lane changing, by integrating risk-aware planning with guaranteed collision avoidance into a unified optimization framework. We first construct a dynamic risk fields (DRF) that captures both the static and dynamic collision risks from surrounding vehicles. Then, we develop a rigorous strategy for generating time-varying convex feasible spaces that ensure kinematic feasibility and safety requirements. The trajectory planning problem is formulated as a finite-horizon optimal control problem and solved using a constrained iterative Linear Quadratic Regulator (iLQR) algorithm that jointly optimizes trajectory smoothness, control effort, and risk exposure while maintaining strict feasibility. Extensive simulations demonstrate that our method outperforms traditional approaches in terms of safety and efficiency, achieving collision-free trajectories with shorter lane-changing distances (28.59 m) and times (2.84 s) while maintaining smooth and comfortable acceleration patterns. In dense roundabout environments the planner further demonstrates robust adaptability, producing larger safety margins, lower jerk, and superior curvature smoothness compared with APF, MPC, and RRT based baselines. These results confirm that the integrated DRF with convex feasible space and constrained iLQR solver provides a balanced solution for safe, efficient, and comfortable trajectory generation in dynamic and interactive traffic scenarios.

Problem

• Develops a trajectory planning pipeline for autonomous lane changing in complex driving scenarios.
• Integrates dynamic risk fields and time-varying convex spaces for collision avoidance.
• Formulates and solves an optimal control problem for safe, efficient, and smooth trajectories.

Innovation

• Dynamic risk fields for static and dynamic collision risks
• Time-varying convex feasible spaces for kinematic feasibility
• Constrained iLQR solver optimizing trajectory smoothness and safety