Receding horizon maneuver generation for automated highway driving

Nilsson, Julia; Falcone, Paolo; Ali, Mohammad; Sjöberg, Jonas · 2015 · OpenAlex-citations

DOI: 10.1016/j.conengprac.2015.04.006

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This paper addresses the challenge of decision-making and control for automated highway driving, specifically focusing on safe lane-change and overtaking maneuvers. Motivated by the high incidence of traffic accidents caused by human error during such maneuvers, the authors propose a method that integrates decision-making and control into a single obstacle avoidance path planning problem. The approach leverages the structured nature of one-way highways to formulate the problem as a convex optimization task within a receding horizon control framework, ensuring real-time computability and collision avoidance guarantees. The methodology employs Model Predictive Control (MPC) to generate trajectories for an ego vehicle relative to surrounding vehicles. The vehicle dynamics are modeled using a simplified point-mass representation, subject to physical constraints such as speed limits, lane boundaries, and acceleration/jerk limits to ensure ride comfort. A key innovation is the formulation of collision avoidance constraints as affine inequalities, specifically Forward Collision Avoidance Constraints (FCC) and Rear Collision Avoidance Constraints (RCC). To maintain the problem’s convexity and avoid the computational complexity of mixed-integer programming, the authors introduce slack variables that relax these constraints based on the relative longitudinal position of the vehicles. Two approaches for handling these slack variables are presented: one modifies the cost function and adds logical constraints to activate FCC or RCC appropriately, while the other uses a more complex cost function structure, though the former is preferred for its robustness and convexity preservation. The study demonstrates the effectiveness of the proposed approach through simulations involving traffic scenarios on a two-lane, one-way road with one and two surrounding vehicles. The results show that the algorithm successfully generates appropriate, traffic-dependent maneuvers, such as adjusting velocity to follow a leading vehicle or executing safe overtaking maneuvers when adjacent lanes are clear. The convex Quadratic Program formulation allows for efficient real-time solution using standard solvers. The simulations confirm that the ego vehicle maintains desired velocity and lane position while strictly adhering to safety margins and physical limitations, effectively avoiding collisions with surrounding vehicles. The significance of this work lies in its ability to provide a computationally efficient, real-time solution for automated driving decision-making that guarantees collision avoidance through convex optimization. By avoiding non-convex or mixed-integer formulations, the method ensures stability and verifiability, which are often lacking in grid-based or potential field approaches. This contributes to the development of reliable Advanced Driver Assistance Systems and fully automated vehicles capable of handling complex highway traffic scenarios safely and efficiently.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• situational awareness

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Theoretical Contribution: computational model