Hybrid A*-Based Reverse Path-Planning of a Vehicle with Trailer System

• URL: http://arxiv.org/abs/2604.24606v1
• Date: Mon, 27 Apr 2026 15:32:05 GMT
• Title: Hybrid A*-Based Reverse Path-Planning of a Vehicle with Trailer System
• Abstract summary: This paper proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle-trailer system as well as collision avoidance considerations.<n>One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Reverse parking maneuvering of a vehicle with trailer system is a difficult task to complete for human drivers due to the multi-body nature of the system and the unintuitive controls required to orientate the trailer properly. The problem is complicated with the presence of other vehicles that the trailer and its connected vehicle must avoid during the reverse parking maneuver. While path planning methods in reverse motion for vehicles with trailers exist, there is a lack of results that also offer collision avoidance as part of the algorithm. This paper hence proposes a modified Hybrid A*-based algorithm that can accommodate the vehicle-trailer system as well as collision avoidance considerations with the other vehicles and obstacles in the parking environment. One of the novelties of this proposed approach is its adaptability to the vehicle with trailer system, where limits of usable steering input that prevent the occurrence of jackknife incidents vary with respect to system configuration. The other contribution is the addition of the collision avoidance functionality which the standard Hybrid A* algorithm lacks. The method is developed and presented first, followed by simulation case studies to demonstrate the efficacy of the proposed approach.

Related papers

• Parking Assistance for Trailer-Truck Transport Vehicles Using Sensor Fusion and Motion Planning [0.0]
  Low-speed maneuvers such as parking remain among the most difficult challenges for autonomous systems.<n>This paper presents a proposed framework for autonomous truck parking that integrates perception, motion planning, control systems, and infrastructure awareness.
  arXiv  Detail & Related papers  (2026-05-04T15:18:39Z)
• Trajectory Planning for an Articulated Commercial Vehicle using Model Predictive Contouring Control [0.82208199207543]
  This paper presents a trajectory planning method for articulated commercial vehicles, specifically tractor-semitrailers.<n>Although MPCC has proven effective for passenger cars, it is generally ill-suited for tractor-semitrailers.
  arXiv  Detail & Related papers  (2026-04-27T05:40:01Z)
• Modified Hybrid A* Collision-Free Path-Planning for Automated Reverse Parking [0.0]
  Parking a vehicle in tight spaces is a challenging task due to the scarcity of feasible paths that are also collision-free.<n>This paper presents a strategy to tackle this kind of maneuver with a modified Hybrid-A* path-planning algorithm.
  arXiv  Detail & Related papers  (2025-12-12T20:28:44Z)
• Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle [1.757194730633422]
  A novel approach to detect road surface anomalies by visual tracking of a preceding vehicle is proposed.<n>The method is versatile, predicting any kind of road anomalies, such as potholes, bumps, debris, etc., unlike direct observation methods that rely on training visual detectors of those cases.
  arXiv  Detail & Related papers  (2025-05-07T13:17:05Z)
• A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency Losses [68.68514648185828]
  Trajectory prediction is essential for the safety and efficiency of planning in autonomous vehicles.<n>Current models often fail to fully capture complex traffic rules and the complete range of potential vehicle movements.<n>This study introduces three novel loss functions: Offroad Loss, Direction Consistency Error, and Diversity Loss.
  arXiv  Detail & Related papers  (2024-11-29T14:47:08Z)
• Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving [45.18582668677648]
  Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving. We propose a lightweight equivariant planning model that generates multi-modal joint predictions for all vehicles. We also propose equivariant route attraction to guide the ego vehicle along a high-level route provided by an off-the-shelf GPS navigation system.
  arXiv  Detail & Related papers  (2024-03-17T18:53:46Z)
• RACE-SM: Reinforcement Learning Based Autonomous Control for Social On-Ramp Merging [0.0]
  Existing non-learning based solutions for vehicle control rely on rules and optimization primarily. Recent advancements in Deep Reinforcement Learning have shown promise and have received significant academic interest. A novel learning based model for acceleration and lane change decision making that explicitly considers the utility to both the ego vehicle and its surrounding vehicles.
  arXiv  Detail & Related papers  (2024-03-05T23:03:56Z)
• The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review [43.30610493968783]
  We review state-of-the-art deep learning-based planning systems, and focus on how they integrate prediction. We discuss the implications, strengths, and limitations of different integration principles.
  arXiv  Detail & Related papers  (2023-08-10T17:53:03Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)
• Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs) [61.94975011711275]
  We propose a geometrically based motion planning technique textquotedblleft RRT*textquotedblright; for this purpose. In the proposed technique, we modified original RRT* introducing an adaptive search space and a steering function. We have tested the proposed technique in various simulated environments.
  arXiv  Detail & Related papers  (2020-08-29T09:55:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.