Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles

2026-07-05

AI Summary

This work addresses the challenge of autonomous vehicle path planning by proposing a staged framework that balances safety, comfort, dynamic feasibility, and computational efficiency. The approach first generates a coarse trajectory using Dijkstra’s graph search, then constructs a spatially varying convex lateral safety corridor. This discrete obstacle-avoidance result is explicitly embedded into a model predictive control (MPC) formulation as continuous feasible constraints. To refine the trajectory, the MPC optimization incorporates the third derivative of lateral offset as a smoothness penalty term. Evaluated across multiple overtaking scenarios, the method significantly reduces lateral acceleration, curvature, and jerk while improving computational performance—achieving 28.08% and 29.52% reductions in computation time on straight and curved road segments, respectively.

Abstract

Path planning is a fundamental component of autonomous vehicles, where achieving safe, comfortable, and dynamically feasible paths while ensuring computational efficiency remains a significant challenge. This paper presents a sequential path planning framework in which a rough path obtained from graph search is explicitly exploited to guide a Model Predictive Control (MPC)-based path refinement. A rough path is first obtained via Dijkstra search on a discretized grid and is then used to construct a spatially varying convex lateral safety corridor that explicitly captures obstacle avoidance constraints, transforming discrete obstacle avoidance decisions into continuous feasibility constraints for optimization. Within this corridor, an MPC problem is formulated to refine the path, enabling efficient optimization while maintaining path smoothness by penalizing the third-order spatial derivative of the lateral offset over a prediction horizon. The proposed algorithm is evaluated in multiple overtaking scenarios on both straight and curved roads, including cases with single and multiple target vehicles, using high-fidelity environment simulations (i.e., CarMaker). Compared with the previous study, which used polynomial fitting and a quadratic programming method, the proposed approach consistently achieves lower lateral acceleration, curvature, and jerk while reducing computational cost by 28.08% on straight roads and 29.52% on curved roads. These results demonstrate that exploiting graph-search structure within an MPC formulation provides an effective balance between path smoothness and computational efficiency for autonomous vehicles in structured driving environments.

Problem

• path planning
• autonomous vehicles
• computational efficiency
• path smoothness
• obstacle avoidance

Innovation

• Graph Search
• Model Predictive Control
• Safety Corridor
• Path Smoothing
• Autonomous Vehicles