An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment

Zhang, Feng; Xia, Ranfei; Chen, Xinxing

doi:10.1177/1729881420909603

Cited by 11 publications

(6 citation statements)

References 27 publications

(29 reference statements)

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“…By searching for a minimum cost path in this graph, the desired lateral path can be found. Graph theory-based search strategies are restricted to optimize only over a limited group of paths [124]. Examples of graph theory-based search techniques are Dijkstra Algorithm, A-Star Algorithm (A * ), and State Lattice Algorithm.…”

Section: ) Graph-theory-based Plannersmentioning

confidence: 99%

Platoon Transitional Maneuver Control System: A Review

et al. 2021

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Connectivity and autonomy are considered two of the most promising technologies to improve mobility, fuel consumption, travel time, and traffic safety in the automated transportation industry. These benefits can be realized through vehicle platooning. A vehicle platoon is composed of a group of connected automated vehicles (CAVs) traveling together at consensual speed, following the leading vehicle (leader) while maintaining a prespecified inter-vehicle distance. This paper reviews the different existing control techniques associated with the transitional platoon maneuvers such as merge/split and lane change. Different longitudinal and lateral vehicle dynamics that are mainly used in the transitional platoon maneuvers are discussed. The most used control algorithms for both longitudinal and lateral control used for transitional platoon maneuvers are reviewed and the advantages and limitations of each control strategy are discussed. The most recent articles on platoon control maneuvers have been analyzed based on the proposed control algorithm, homogeneously or heterogeneously of platoon members, type of platoon maneuver, the aim of control problem, type of implementation, and used simulation tools. This paper also discusses different trajectory planning techniques used in lateral motion control and studies the most recent research related to trajectory planning for automated vehicles and summarizes them based on the used trajectory planning technique, platoon or/and lane change, the type of traffic, and the cost functions. Finally, this paper explores the open issues and directions for future research.

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Section: ) Graph-theory-based Plannersmentioning

confidence: 99%

Platoon Transitional Maneuver Control System: A Review

et al. 2021

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“…The Dijkstra algorithm, a heuristic search algorithm for finding the single-source shortest path, can be viewed as a simplified version of the A* algorithm. However, it suffers from similar issues of low efficiency, occasional lack of optimal solutions, and high memory overhead [16] [17]. Although the DP algorithm is effective for solving multilayer planning problems, it has some limitations.…”

Section: Introductionmentioning

confidence: 99%

On-Road Trajectory Planning of Connected and Automated Vehicles in Complex Traffic Settings: A Hierarchical Framework of Trajectory Refinement

Zhao,

Han,

Cui

et al. 2024

IEEE Access

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This paper presents a hierarchical framework for on-road trajectory planning in complex traffic environments. Firstly, the processing of sparse coarse trajectories involves the utilization of DP (Dynamic Programming) generation and interpolation techniques. Then, for the waypoints with collision risk in the smoothed trajectory, the spiral search method is used to find some safe alternate waypoints. The alternate waypoints and the previous ones without collision risk form the amended trajectory. Concurrently, safety tunnels are constructed along the amended trajectory for the ego vehicle. Furthermore, with the constraint conditions of vehicle kinematics model and safety tunnels, nonlinear program (NLP) optimization is carried out for the amended trajectory of ego vehicle to obtain smooth and safe trajectories. For typical cases, simulation experiments demonstrate that the ego vehicle can ensure collision safety in dynamic traffic scenarios, while maintaining smooth vehicle velocity and small jitter of the front wheel angle. The proposed trajectory planning framework provides a novel decision-making method for connected and automated vehicles (CAVs).INDEX TERMS Trajectory planning, nonlinear program optimization, connected and automated vehicles.

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“…The proposed reinforcement learning fuzzy system is also promising to be applied to other robotic problems, for example, human-robot interactions [28][29][30][31], environment adaptation of robots [32,33], calibration [34][35][36], path planning and control [37][38][39][40]. The fuzzy system-based controllers can be initialized with expert knowledge, trained with reinforcement learning in simulated scenarios, and finally adapted to the real world.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking

et al. 2022

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In dynamic outdoor environments characterized by turbulent airflow and intermittent odor plumes, robotic odor plume tracking remains challenging, because existing algorithms heavily rely on manually tuning or learning from expert experience, which are hard to implement in an unknown environment. In this paper, a multi-continuous-output Takagi–Sugeno–Kang fuzzy system was designed and tuned with reinforcement learning to solve the robotic odor source localization problem in dynamic odor plumes. Based on the Lévy Taxis plume tracking controller, the proposed fuzzy system determined the parameters of the controller based on the robot’s observation and guided the robot to turn and move towards the odor source at each searching step. The trained fuzzy system was tested in simulated filament-based odor plumes dispersed by a changing wind field. The results showed that the performance of the proposed fuzzy system-based controller trained with reinforcement learning can achieve a similar success rate and higher efficiency compared with a manually tuned and well-designed fuzzy system-based controller. The fuzzy system-based plume tracking controller was also validated through real robotic experiments.

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An optimal trajectory planning algorithm for autonomous trucks: Architecture, algorithm, and experiment

Cited by 11 publications

References 27 publications

Platoon Transitional Maneuver Control System: A Review

Platoon Transitional Maneuver Control System: A Review

On-Road Trajectory Planning of Connected and Automated Vehicles in Complex Traffic Settings: A Hierarchical Framework of Trajectory Refinement

A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking

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