An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

Chen, Ruinan; Hu, Jie; Xu, Wendao

doi:10.3390/app122311982

Cited by 17 publications

(9 citation statements)

References 26 publications

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“…These rules are used to determine the driving behavior of autonomous vehicles based on different environmental cues [ 13 ]. After receiving driving decision commands, Chen et al employed a Dijkstra-based method to plan feasible driving trajectories [ 14 ]. Yu et al [ 15 ] considered constraints from surrounding vehicles during lane-changing maneuvers and utilized a third-degree polynomial to plan intelligent vehicle lane-change trajectories.…”

Section: Related Workmentioning

confidence: 99%

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Liu,

Diao

2024

PLoS ONE

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As autonomous driving technology continues to advance and gradually become a reality, ensuring the safety of autonomous driving in complex traffic scenarios has become a key focus and challenge in current research. Model-free deep reinforcement learning (Deep Reinforcement Learning) methods have been widely used for addressing motion planning problems in complex traffic scenarios, as they can implicitly learn interactions between vehicles. However, current planning methods based on deep reinforcement learning exhibit limited robustness and generalization performance. They struggle to adapt to traffic conditions beyond the training scenarios and face difficulties in handling uncertainties arising from unexpected situations. Therefore, this paper addresses the challenges presented by complex traffic scenarios, such as signal-free intersections. It does so by first utilizing the historical trajectories of adjacent vehicles observed in these scenarios. Through a Variational Auto-Encoder (VAE) based on the Gated Recurrent Unit (GRU) recurrent neural network, it extracts driver style features. These driver style features are then integrated with other state parameters and used to train a motion planning strategy within an extended reinforcement learning framework. This approach ultimately yields a more robust and interpretable mid-to-mid motion planning method. Experimental results confirm that the proposed method achieves low collision rates, high efficiency, and successful task completion in complex traffic scenarios.

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Section: Related Workmentioning

confidence: 99%

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

Liu,

Diao

2024

PLoS ONE

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“…Local path planning algorithms include the artificial potential field algorithm (APF) [15], DWA [16], and deep learning method (DL) [17]. Dijkstra [18] uses a breadth-first search, which has the advantages of simple principles and a small amount of computation, but the computational efficiency is low. RRT [19] is a sampling-based algorithm with probabilistic integrity, but the algorithm search is blind, and the convergence speed is slow.…”

Section: Related Workmentioning

confidence: 99%

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

Zhang,

Yang,

Zhou

et al. 2024

Applied Sciences

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In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.

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“…Path-planning algorithms for UGVs can be mainly divided into traditional classical algorithms [8,9] and learning-based methods [10][11][12]. Traditional classic algorithms generally obtain the optimized path in complex environments through a hierarchical architecture incorporating a global planner and a local planner [13].…”

Section: Introductionmentioning

confidence: 99%

Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning

Zhang,

Fan,

Zhou

et al. 2024

Machines

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Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning techniques have been employed for this purpose, planners utilizing reinforcement learning (RL) exhibit superior adaptability within exceedingly complex and dynamic environments. Nevertheless, existing RL-based path planners encounter several shortcomings, notably, redundant map representations, inadequate feature extraction, and limited adaptiveness across diverse environments. In response to these challenges, this paper proposes an innovative and highly self-adaptive path-planning approach based on Transformer encoder feature extraction coupled with incremental reinforcement learning (IRL). Initially, an autoencoder is utilized to compress redundant map representations, providing the planner with sufficient environmental data while minimizing dimensional complexity. Subsequently, the Transformer encoder, renowned for its capacity to analyze global long-range dependencies, is employed to capture intricate correlations among UGV statuses at continuous intervals. Finally, IRL is harnessed to enhance the path planner’s generalization capabilities, particularly when the trained agent is deployed in environments distinct from its training counterparts. Our empirical findings demonstrate that the proposed method outperforms traditional uniform-sampling-based approaches in terms of execution time, path length, and trajectory smoothness. Furthermore, it exhibits a fivefold increase in adaptivity compared to conventional transfer-learning-based fine-tuning methodologies.

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An RRT-Dijkstra-Based Path Planning Strategy for Autonomous Vehicles

Cited by 17 publications

References 26 publications

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

An automatic driving trajectory planning approach in complex traffic scenarios based on integrated driver style inference and deep reinforcement learning

A Path Planning Method Based on Improved A* and Fuzzy Control DWA of Underground Mine Vehicles

Highly Self-Adaptive Path-Planning Method for Unmanned Ground Vehicle Based on Transformer Encoder Feature Extraction and Incremental Reinforcement Learning

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