Research on Intelligent Vehicle Path Planning Algorithm

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 31 October 2024 | Viewed by 15864

Special Issue Editors

School of Automobile and Rail Transportation, Nanjing Institute of Technology, Nanjing 211167, China
Interests: vehicle dynamics; new energy vehicles and intelligent technology
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Guest Editor
School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
Interests: vehicle engineering; vibration mechanics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To solve various problems such as traffic congestion, traffic safety and environmental pollution on urban roads, path planning algorithms have gradually become an important research direction in the field of intelligent transportation, receiving considerable scholarly attention. In recent years, the application and popularity of intelligent transportation has provided a rich variety of research topics for path planning algorithms. Currently, research in this are focuses on various path planning algorithms such as traditional algorithms, intelligent optimization algorithms, algorithms based on reinforcement learning, and hybrid algorithms. In order to promote academic exchanges in related technology directions and the development of advanced technologies for intelligent transportation, we are launching a Special Issue of the World Electronic Vehicle Journal on “Research on Intelligent Vehicle Path Planning” to call for papers. We encourage authors to submit research discussing the core key technical problems, the future research trends and methods of path planning algorithms in intelligent transportation.

Dr. Liguo Zang
Dr. Leilei Zhao
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent vehicle
  • path planning algorithm
  • algorithmic optimization
  • obstacle avoidance
  • lane change

Published Papers (12 papers)

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Research

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30 pages, 9113 KiB  
Article
Research on Unmanned Vehicle Path Planning Based on the Fusion of an Improved Rapidly Exploring Random Tree Algorithm and an Improved Dynamic Window Approach Algorithm
by Shuang Wang, Gang Li and Boju Liu
World Electr. Veh. J. 2024, 15(7), 292; https://doi.org/10.3390/wevj15070292 - 30 Jun 2024
Viewed by 355
Abstract
Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that [...] Read more.
Aiming at the problem that the traditional rapidly exploring random tree (RRT) algorithm only considers the global path of unmanned vehicles in a static environment, which has the limitation of not being able to avoid unknown dynamic obstacles in real time, and that the traditional dynamic window approach (DWA) algorithm is prone to fall into a local optimum during local path planning, this paper proposes a path planning method for unmanned vehicles that integrates improved RRT and DWA algorithms. The RRT algorithm is improved by introducing strategies such as target-biased random sampling, adaptive step size, and adaptive radius node screening, which enhance the efficiency and safety of path planning. The global path key points generated by the improved RRT algorithm are used as the subtarget points of the DWA algorithm, and the DWA algorithm is optimized through the design of an adaptive evaluation function weighting method based on real-time obstacle distances to achieve more reasonable local path planning. Through simulation experiments, the fusion algorithm shows promising results in a variety of typical static and dynamic mixed driving scenarios, can effectively plan a path that meets the driving requirements of an unmanned vehicle, avoids unknown dynamic obstacles, and shows higher path optimization efficiency and driving stability in complex environments, which provides strong support for an unmanned vehicle’s path planning in complex environments. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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23 pages, 15041 KiB  
Article
Research on Obstacle Avoidance Trajectory Planning for Autonomous Vehicles on Structured Roads
by Yunlong Li, Gang Li and Kang Peng
World Electr. Veh. J. 2024, 15(4), 168; https://doi.org/10.3390/wevj15040168 - 17 Apr 2024
Cited by 1 | Viewed by 1099
Abstract
This paper focuses on the obstacle avoidance trajectory planning problem for autonomous vehicles on structured roads. The objective is to design a trajectory planning algorithm that can ensure vehicle safety and comfort and satisfy the rationality of traffic regulations. This paper proposes a [...] Read more.
This paper focuses on the obstacle avoidance trajectory planning problem for autonomous vehicles on structured roads. The objective is to design a trajectory planning algorithm that can ensure vehicle safety and comfort and satisfy the rationality of traffic regulations. This paper proposes a path and speed decoupled planning method for non-split vehicle trajectory planning on structured roads. Firstly, the path planning layer adopts the improved artificial potential field method. The obstacle-repulsive potential field, gravitational potential field, and fitting method of the traditional artificial potential field are improved. Secondly, the speed planning aspect is performed in the Frenet coordinate system. Speed planning is accomplished based on S-T graph construction and solving convex optimization problems. Finally, simulation and experimental verification are performed. The results show that the method proposed in this paper can significantly improve the safety and comfortable riding of the vehicle. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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21 pages, 15213 KiB  
Article
Omnidirectional AGV Path Planning Based on Improved Genetic Algorithm
by Qinyu Niu, Yao Fu and Xinwei Dong
World Electr. Veh. J. 2024, 15(4), 166; https://doi.org/10.3390/wevj15040166 - 16 Apr 2024
Cited by 1 | Viewed by 946
Abstract
To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This [...] Read more.
To address the issues with traditional genetic algorithm (GA) path planning, which often results in redundant path nodes and local optima, we propose an Improved Genetic Algorithm that incorporates an ant colony algorithm (ACO). Firstly, a new population initialization method is proposed. This method adopts a higher-quality random point generation strategy to generate random points centrally near the start and end of connecting lines. It combines the improved ACO algorithm to connect these random points quickly, thus greatly improving the quality of the initial population. Secondly, path smoothness constraints are proposed in the adaptive function. These constraints reduce the large-angle turns and non-essential turns, improving the smoothness of the generated path. The algorithm integrates the roulette and tournament methods in the selection stage to enhance the searching ability and prevent premature convergence. Additionally, the crossover stage introduces the edit distance and a two-layer crossover operation based on it to avoid ineffective crossover and improve convergence speed. In the mutation stage, we propose a new mutation method and introduce a three-stage mutation operation based on the idea of simulated annealing. This makes the mutation operation more effective and efficient. The three-stage mutation operation ensures that the mutated paths also have high weights, increases the diversity of the population, and avoids local optimality. Additionally, we added a deletion operation to eliminate redundant nodes in the paths and shorten them. The simulation software and experimental platform of ROS (Robot Operating System) demonstrate that the improved algorithm has better path search quality and faster convergence speed. This effectively prevents the algorithm from maturing prematurely and proves its effectiveness in solving the path planning problem of AGV (automated guided vehicle). Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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16 pages, 2311 KiB  
Article
Adaptive MPC-Based Lateral Path-Tracking Control for Automatic Vehicles
by Shaobo Yang, Yubin Qian, Wenhao Hu, Jiejie Xu and Hongtao Sun
World Electr. Veh. J. 2024, 15(3), 95; https://doi.org/10.3390/wevj15030095 - 4 Mar 2024
Viewed by 1493
Abstract
For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new [...] Read more.
For continuously changing road conditions and vehicle operating states, the exactitude of vehicle path tracking has not been secured by model predictive control based on linear lateral stiffness. An amended square root cubature Kalman filter method based on the minimization of a new covariance of interest is proposed to calculate the tire lateral deflection force in real time. The ratio of the estimated tire force to the linear tire force was used as a ratio to adjust the lateral deflection stiffness, and an adaptive model predictive controller was built based on the vehicle path-tracking error model to correct the tire lateral deflection stiffness. Finally, an analysis based on the joint CarSim and Simulink simulation platform shows that compared to a conventional model predictive control (MPC) controller, a trajectory-following controller built based on this method can effectively reduce the lateral distance error and heading error of an autonomous vehicle. Especially under low adhesion conditions, the conventional MPC controllers will demonstrate large instability during trajectory tracking due to the deviation of the linear tire force calculation results, whereas the adaptive model predictive control (AMPC) controllers can correct the side deflection stiffness by estimating the tire force and still achieve stable and effective tracking of the target trajectory. This suggests that the proposed algorithm can improve the effectiveness of trajectory tracking control for autonomous vehicles, which is an important reference value for the optimization of autonomous vehicle control systems. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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23 pages, 744 KiB  
Article
Heuristic Algorithms for Heterogeneous and Multi-Trip Electric Vehicle Routing Problem with Pickup and Delivery
by Li Wang, Yifan Ding, Zhiyuan Chen, Zhiyuan Su and Yufeng Zhuang
World Electr. Veh. J. 2024, 15(2), 69; https://doi.org/10.3390/wevj15020069 - 15 Feb 2024
Viewed by 1436
Abstract
In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the [...] Read more.
In light of the widespread use of electric vehicles for urban distribution, this paper delves into the electric vehicle routing problem (EVRP): specifically addressing multiple trips per vehicle, diverse vehicle types, and simultaneous pickup and delivery. The primary objective is to minimize the overall cost, which encompasses travel expenses, waiting times, recharging costs, and fixed vehicle costs. The focal problem is formulated as a heterogeneous and multi-trip electric vehicle routing problem with pickup and delivery (H-MT-EVRP-PD). Additionally, we introduce two heuristic algorithms to efficiently approximate solutions within a reasonable computational time. The variable neighborhood search (VNS) algorithm and the adaptive large neighborhood search (ALNS) algorithm are presented and compared based on our computational experiences with both. Through solving a series of large-scale real-world instances for the H-MT-EVRP-PD and smaller instances using an exact method, we demonstrate the efficacy of the proposed approaches. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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21 pages, 6890 KiB  
Article
Obstacle Avoidance Trajectory Planning for Autonomous Vehicles on Urban Roads Based on Gaussian Pseudo-Spectral Method
by Zhenfeng Li, Xuncheng Wu, Weiwei Zhang and Wangpengfei Yu
World Electr. Veh. J. 2024, 15(1), 7; https://doi.org/10.3390/wevj15010007 - 26 Dec 2023
Viewed by 1806
Abstract
Urban autonomous vehicles on city roads are subject to various constraints when changing lanes, and commonly used trajectory planning methods struggle to describe these conditions accurately and directly. Therefore, generating accurate and adaptable trajectories is crucial for safer and more efficient trajectory planning. [...] Read more.
Urban autonomous vehicles on city roads are subject to various constraints when changing lanes, and commonly used trajectory planning methods struggle to describe these conditions accurately and directly. Therefore, generating accurate and adaptable trajectories is crucial for safer and more efficient trajectory planning. This study proposes an optimal control model for local path planning that integrates dynamic vehicle constraints and boundary conditions into the optimization problem’s constraint set. Using the lane-changing scenario as a basis, this study establishes environmental and collision avoidance constraints during driving and develops a performance metric that optimizes both time and turning angle. The study employs the Gauss pseudo-spectral method to continuously discretize the state and control variables, converting the optimal control problem into a nonlinear programming problem. Using numerical solutions, variable control and state trajectories that satisfy multiple constraint conditions while optimizing the performance metric are generated. The study employs two weights in the experiment to evaluate the method’s performance, and the findings demonstrate that the proposed method guarantees safe obstacle avoidance, is stable, and is computationally efficient at various interpolation points compared to the Legendre pseudo-spectral method (LPM) and the Shooting method. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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26 pages, 4313 KiB  
Article
Utilizing Probabilistic Maps and Unscented-Kalman-Filtering-Based Sensor Fusion for Real-Time Monte Carlo Localization
by Wael A. Farag and Julien Moussa H. Barakat
World Electr. Veh. J. 2024, 15(1), 5; https://doi.org/10.3390/wevj15010005 - 21 Dec 2023
Viewed by 1534
Abstract
An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and [...] Read more.
An autonomous car must know where it is with high precision in order to maneuver safely and reliably in both urban and highway environments. Thus, in this paper, a reliable and relatively precise position estimation (localization) technique for autonomous vehicles is proposed and implemented. In dealing with the obtained sensory data or given knowledge about the vehicle’s surroundings, the proposed method takes a probabilistic approach. In this approach, the involved probability densities are expressed by keeping a collection of samples selected at random from them (Monte Carlo simulation). Consequently, this Monte Carlo sampling allows the resultant position estimates to be represented with any arbitrary distribution, not only a Gaussian one. The selected technique to implement this Monte-Carlo-based localization is Bayesian filtering with particle-based density representations (i.e., particle filters). The employed particle filter receives the surrounding object ranges from a carefully tuned Unscented Kalman Filter (UKF) that is used to fuse radar and lidar sensory readings. The sensory readings are used to detect pole-like static objects in the egocar’s surroundings and compare them to the ones that exist in a supplied detailed reference map that contains pole-like landmarks that are produced offline and extracted from a 3D lidar scan. Comprehensive simulation tests were conducted to evaluate the outcome of the proposed technique in both lateral and longitudinal localization. The results show that the proposed technique outperforms the other techniques in terms of smaller lateral and longitudinal mean position errors. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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21 pages, 752 KiB  
Article
A GRASP Approach for the Energy-Minimizing Electric Vehicle Routing Problem with Drones
by Nikolaos A. Kyriakakis, Themistoklis Stamadianos, Magdalene Marinaki and Yannis Marinakis
World Electr. Veh. J. 2023, 14(12), 354; https://doi.org/10.3390/wevj14120354 - 18 Dec 2023
Viewed by 1765
Abstract
This study addresses the Electric Vehicle Routing Problem with Drones (EVRPD) by implementing and comparing two variants of the Greedy Randomized Adaptive Search Procedure (GRASP). The primary objective of the EVRPD is to optimize the routing of a combined fleet of ground and [...] Read more.
This study addresses the Electric Vehicle Routing Problem with Drones (EVRPD) by implementing and comparing two variants of the Greedy Randomized Adaptive Search Procedure (GRASP). The primary objective of the EVRPD is to optimize the routing of a combined fleet of ground and aerial vehicles, with the aim of improving delivery efficiency and minimizing energy consumption, which is directly influenced by the weight of the packages. The study assumes a standardized packing system consisting of three weight classes, where deliveries are exclusively performed by drones, while ground vehicles function as mobile depots. The two employed GRASP variants vary in their methods of generating the Restricted Candidate List (RCL), with one utilizing a cardinality-based RCL and the other adopting a value-based RCL. To evaluate their performance, benchmark instances from the existing EVRPD literature are utilized, extensive computational experiments are conducted, and the obtained computational results are compared and discussed. The findings of the research highlight the considerable impact of RCL generation strategies on solution quality. Lastly, the study reports four new best-known values. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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19 pages, 4083 KiB  
Article
Research on Intelligent Vehicle Motion Planning Based on Pedestrian Future Trajectories
by Pan Liu, Guoguo Du, Yongqiang Chang and Minghui Liu
World Electr. Veh. J. 2023, 14(12), 320; https://doi.org/10.3390/wevj14120320 - 23 Nov 2023
Viewed by 1496
Abstract
This work proposes an improved pedestrian social force model for pedestrian trajectory prediction to prevent intelligent vehicles from colliding with pedestrians while driving on the road. In this model, the intelligent vehicle performs motion planning on the basis of predicted pedestrian trajectory results. [...] Read more.
This work proposes an improved pedestrian social force model for pedestrian trajectory prediction to prevent intelligent vehicles from colliding with pedestrians while driving on the road. In this model, the intelligent vehicle performs motion planning on the basis of predicted pedestrian trajectory results. A path is planned by using the fifth-order Bezier curve, the optimal coordinate is acquired by adjusting the weight coefficient of each optimisation goal, and the optimal driving trajectory curve is planned. In speed planning, the pedestrian collision boundary is proposed to ensure pedestrian safety. The initial speed planning is performed by a dynamic programming algorithm, and then the optimal speed curve is obtained by quadratic programming. Finally, the front pedestrian deceleration or uniform speed scene is set for simulation verification. Simulation results show that the vehicle speed reaches a maximum value of 6.39 m/s under the premise of ensuring safety and that the average speed of the intelligent vehicle is 4.6 m/s after a normal start process. The maximum and average speed values obtained with trajectory prediction indicate that the intelligent vehicle ensures pedestrian and vehicle safety as well as the intelligent vehicle’s economy. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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21 pages, 9112 KiB  
Article
Study on Lane-Change Replanning and Trajectory Tracking for Intelligent Vehicles Based on Model Predictive Control
by Yaohua Li, Dengwang Zhai, Jikang Fan and Guoqing Dong
World Electr. Veh. J. 2023, 14(9), 234; https://doi.org/10.3390/wevj14090234 - 24 Aug 2023
Cited by 1 | Viewed by 1459
Abstract
When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which [...] Read more.
When an intelligent vehicle changes lanes, the state of other vehicles may change, which increases the risk of collision. Therefore, real-time local path replanning is needed at this time. Based on model predictive control (MPC), a lane-change trajectory replanning strategy was proposed, which was divided into a lane-change trajectory correction strategy, a lane-change switchback strategy and forward active collision avoidance strategy according to collision risk. Based on the collision risk function of the rectangular safety neighborhood, the objective functions were designed according to the specific requirements of different strategies. The vehicle lateral controller based on MPC and the vehicle longitudinal motion controller were established. The longitudinal velocity was taken as the joint point to establish the lateral and longitudinal integrated controller. The trajectory planning module, trajectory replanning module and trajectory tracking module were integrated in layers, and the three trajectory replanning strategies of lane-change trajectory correction, lane-change switchback and forward active collision avoidance were respectively simulated and verified. The simulation results showed the trajectory replanning strategy achieves collision avoidance under different scenarios and ensures the vehicle’s driving stability. The trajectory tracking layer achieves accurate tracking of the conventional lane-change trajectory and has good driving stability and comfort. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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17 pages, 2561 KiB  
Article
Electric Logistics Vehicle Path Planning Based on the Fusion of the Improved A-Star Algorithm and Dynamic Window Approach
by Mengxue Yu, Qiang Luo, Haibao Wang and Yushu Lai
World Electr. Veh. J. 2023, 14(8), 213; https://doi.org/10.3390/wevj14080213 - 10 Aug 2023
Cited by 5 | Viewed by 1363
Abstract
The study of path-planning algorithms is crucial for an electric logistics vehicle to reach its target point quickly and safely. In light of this, this work suggests a novel path-planning technique based on the improved A-star (A*) fusion dynamic window approach (DWA). First, [...] Read more.
The study of path-planning algorithms is crucial for an electric logistics vehicle to reach its target point quickly and safely. In light of this, this work suggests a novel path-planning technique based on the improved A-star (A*) fusion dynamic window approach (DWA). First, compared to the A* algorithm, the upgraded A* algorithm not only avoids the obstruction border but also removes unnecessary nodes and minimizes turning angles. Then, the DWA algorithm is fused with the enhanced A* algorithm to achieve dynamic obstacle avoidance. In addition to RVIZ of ROS, MATLAB simulates and verifies the upgraded A* algorithm and the A* fused DWA. The MATLAB simulation results demonstrate that the approach based on the enhanced A* algorithm combined with DWA not only shortens the path by 4.56% when compared to the A* algorithm but also smooths the path and has dynamic obstacle-avoidance capabilities. The path length is cut by 8.99% and the search time is cut by 16.26% when compared to the DWA. The findings demonstrate that the enhanced method in this study successfully addresses the issues that the A* algorithm’s path is not smooth, dynamic obstacle avoidance cannot be performed, and DWA cannot be both globally optimal. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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Review

Jump to: Research

27 pages, 5099 KiB  
Review
Path Planning Algorithms for Smart Parking: Review and Prospects
by Zhonghai Han, Haotian Sun, Junfu Huang, Jiejie Xu, Yu Tang and Xintian Liu
World Electr. Veh. J. 2024, 15(7), 322; https://doi.org/10.3390/wevj15070322 - 20 Jul 2024
Viewed by 245
Abstract
Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively [...] Read more.
Path planning algorithms are crucial components in the process of smart parking. At present, there are many path planning algorithms designed for smart parking. A well-designed path planning algorithm has a significant impact on the efficiency of smart parking. Firstly, this paper comprehensively describes the principles and steps of four types of path planning algorithms: the Dijkstra algorithm (including its optimized derivatives), the A* algorithm (including its optimized derivatives), the RRT (Rapidly exploring Random Trees) algorithm (including its optimized derivatives), and the BFS (Breadth First Search) algorithm. Secondly, the Dijkstra algorithm, the A* algorithm, the BFS algorithm, and the Dynamic Weighted A* algorithm were utilized to plan the paths required for the process of smart parking. During the analysis, it was found that the Dijkstra algorithm had the drawbacks of planning circuitous paths and taking too much time in the path planning for smart parking. Although the traditional A* algorithm based on the Dijkstra algorithm had greatly reduced the planning time, the effect of path planning was still unsatisfactory. The BFS (Breadth First Search) algorithm had the shortest planning time among the four algorithms, but the paths it plans were unstable and not optimal. The Dynamic Weighted A* algorithm could achieve better path planning results, and with adjustments to the weight values, this algorithm had excellent adaptability. This review provides a reference for further research on path planning algorithms in the process of smart parking. Full article
(This article belongs to the Special Issue Research on Intelligent Vehicle Path Planning Algorithm)
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