Modified Hybrid A* Collision-Free Path-Planning for Automated Reverse Parking
- URL: http://arxiv.org/abs/2512.12021v1
- Date: Fri, 12 Dec 2025 20:28:44 GMT
- Title: Modified Hybrid A* Collision-Free Path-Planning for Automated Reverse Parking
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Parking a vehicle in tight spaces is a challenging task to perform due to the scarcity of feasible paths that are also collision-free. This paper presents a strategy to tackle this kind of maneuver with a modified Hybrid-A* path-planning algorithm that combines the feasibility guarantee inherent in the standard Hybrid A* algorithm with the addition of static obstacle collision avoidance. A kinematic single-track model is derived to describe the low-speed motion of the vehicle, which is subsequently used as the motion model in the Hybrid A* path-planning algorithm to generate feasible motion primitive branches. The model states are also used to reconstruct the vehicle centerline, which, in conjunction with an inflated binary occupancy map, facilitates static obstacle collision avoidance functions. Simulation study and animation are set up to test the efficacy of the approach, and the proposed algorithm proves to consistently provide kinematically feasible trajectories that are also collision-free.
Related papers
- AutoPath: Learning Transferable Goal-Conditioned Stochastic Path Prior for Safe Navigation Without Human Demonstrations [103.61864552260316]
Real-time navigation in dynamic environments requires collision-free and dynamically feasible motion under limited perception.<n>We formulate navigation as learning a transferable goal-aligned path prior that models a reusable distribution over goal-aligned geometry-consistent local paths conditioned on local observations.<n>We develop a structured prior learning framework that parameterizes local paths using a geometry-aware polar action manifold and incorporates risk-sensitive utility shaping with multi-goal distributional rollouts for stable and safety-aware planning.
arXiv Detail & Related papers (2026-07-13T16:00:41Z) - Integrated Graph Search and Model Predictive Control for Smooth and Efficient Path Planning in Autonomous Vehicles [1.228397872697275]
A rough path is first obtained via Dijkstra search on a discretized grid.<n>A Model Predictive Control problem is formulated to refine the path.<n>The proposed algorithm is evaluated in multiple overtaking scenarios on both straight and curved roads.
arXiv Detail & Related papers (2026-07-05T12:14:33Z) - PoseShield: Neural Collision Fields for Human Self-Collision Resolution [44.26354057690119]
Self-collision remains a persistent challenge in SMPL-based human pose estimation and motion generation.<n>We propose PoseShield, a neural collision constraint defined directly in SMPL pose space.<n>Our method achieves a 95.8% success rate and outperforms state-of-the-art baselines.
arXiv Detail & Related papers (2026-06-29T01:20:25Z) - Post-Collision Trajectory Restoration for a Single-track Ackermann Vehicle using Heuristic Steering and Tractive Force Functions [1.1899559337707113]
Post-collision trajectory restoration is a safety-critical capability for autonomous vehicles.<n>This paper proposes a structured recovery control law that jointly commands steering and tractive force for a generalized single-track vehicle model.
arXiv Detail & Related papers (2026-02-09T09:57:10Z) - Just in time Informed Trees: Manipulability-Aware Asymptotically Optimized Motion Planning [46.553063931217245]
We introduce the Just-in-Time Informed Trees (JIT*) algorithm, an enhancement over Effort Informed Trees (EIT*)<n>JIT* consistently outperforms traditional sampling-based planners across $mathbbR4$ to $mathbbR16$ dimensions.
arXiv Detail & Related papers (2026-01-27T18:58:51Z) - Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving [55.13109926181247]
We introduce ReflectDrive, a learning-based framework that integrates a reflection mechanism for safe trajectory generation via discrete diffusion.<n>Central to our approach is a safety-aware reflection mechanism that performs iterative self-correction without gradient.<n>Our method begins with goal-conditioned trajectory generation to model multi-modal driving behaviors.
arXiv Detail & Related papers (2025-09-24T13:35:15Z) - Hybrid Imitation-Learning Motion Planner for Urban Driving [0.0]
We propose a novel hybrid motion planner that integrates both learning-based and optimization-based techniques.<n>Our model effectively balances safety and human-likeness, mitigating the trade-off inherent in these objectives.<n>We validate our approach through simulation experiments and further demonstrate its efficacy by deploying it in real-world self-driving vehicles.
arXiv Detail & Related papers (2024-09-04T16:54:31Z) - Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare.
We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues.
We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
arXiv Detail & Related papers (2024-04-11T10:26:40Z) - 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) - Integrating Higher-Order Dynamics and Roadway-Compliance into
Constrained ILQR-based Trajectory Planning for Autonomous Vehicles [3.200238632208686]
Trajectory planning aims to produce a globally optimal route for Autonomous Passenger Vehicles.
Existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories.
We augment this model by higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk.
arXiv Detail & Related papers (2023-09-25T22:30:18Z) - Multi-Agent Chance-Constrained Stochastic Shortest Path with Application
to Risk-Aware Intelligent Intersection [15.149982804527182]
A formidable challenge for existing automated intersections lies in detecting and reasoning about uncertainty from the operating environment and human-driven vehicles.
We propose a risk-aware intelligent intersection system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs)
arXiv Detail & Related papers (2022-10-03T06:49:23Z) - Congestion-aware Multi-agent Trajectory Prediction for Collision
Avoidance [110.63037190641414]
We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework.
By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories.
In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
arXiv Detail & Related papers (2021-03-26T02:42:33Z) - 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.