Trajectory Planning for Autonomous Vehicles: State of the Art

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1175

Special Issue Editor


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Guest Editor
Department of Mechanical Engineering, School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85281, USA
Interests: planning and control autonomous driving; vehicle dynamics; vehicle lateral stability control

Special Issue Information

Dear Colleagues,

Trajectory planning is essential and critical in autonomous driving systems, where safety, comfort, and efficiency must be dynamically balanced. Robust and flexible trajectories should be planned based on onboard environment perceptions and traffic regulations. This Special Issue aims to collect state-of-the-art research and ideas about trajectory planning for all levels of autonomous vehicles. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Trajectory planning for autonomous vehicles;
  • Behavior planning;
  • Decision making;
  • Path planning;
  • Speed planning;
  • End-to-end model-based trajectory planning;
  • Spatial-temporal trajectory planning.

We look forward to receiving your contributions.

Dr. Yiwen Huang
Guest Editor

Manuscript Submission Information

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Keywords

  • trajectory planning for autonomous vehicles
  • behavior planning
  • decision making
  • path planning
  • speed planning
  • end-to-end model-based trajectory planning
  • spatial–temporal trajectory planning

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Published Papers (1 paper)

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Research

18 pages, 4666 KiB  
Article
A Novel Lateral Control System for Autonomous Vehicles: A Look-Down Strategy
by Farzad Nadiri and Ahmad B. Rad
Machines 2025, 13(3), 211; https://doi.org/10.3390/machines13030211 - 6 Mar 2025
Viewed by 704
Abstract
This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, [...] Read more.
This paper introduces a robust yet straightforward lane detection and lateral control approach via the deployment of a dual camera based on the look-down strategy for autonomous vehicles. Unlike traditional single-camera systems that rely on the look-ahead methodology and a single front-facing preview, the proposed algorithm leverages two downward-facing cameras mounted beneath the vehicle’s driver and the passenger side mirror, respectively. This configuration captures the road surface, enabling precise detection of the lateral boundaries, particularly during lane changes and in narrow lanes. A Proportional-Integral-Derivative (PID) controller is designed to maintain the vehicle’s position in the center of the road. We compare this system’s accuracy, lateral steadiness, and computational efficiency against (1) a conventional bird’s-eye view lane detection method and (2) a popular deep learning-based lane detection framework. Experiments in the CARLA simulator under varying road geometries, lighting conditions, and lane marking qualities confirm that the proposed look-down system achieves superior real-time performance, comparable lane detection accuracy, and reduced computational overhead relative to both traditional bird’s-eye and advanced neural approaches. These findings underscore the practical benefits of a straightforward, explainable, and resource-efficient solution for robust autonomous vehicle lane-keeping. Full article
(This article belongs to the Special Issue Trajectory Planning for Autonomous Vehicles: State of the Art)
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