Modelling Driver’s Behaviour While Avoiding Obstacles

Jurecki, Rafał; Stańczyk, Tomasz

doi:10.3390/app13010616

Cited by 3 publications

(2 citation statements)

References 63 publications

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“…This method generates repulsive forces between all obstacles in the environment and the vehicle, whereas the goal point generates an attractive force. By searching for directions where the potential field weakens, feasible paths can be planned [38][39][40][41][42]. The overall planning concept is shown in Figure 4.…”

Section: Artificial Potential Field Methodsmentioning

confidence: 99%

A Review of the Motion Planning and Control Methods for Automated Vehicles

Gao

Ding

et al. 2023

Sensors

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The motion planning and control method of automated vehicles, as the key technology of automated vehicles, directly affects the safety, comfort, and other technical indicators of vehicles. The planning module is responsible for generating a vehicle driving path. The control module is responsible for driving the vehicle. In this study, we review the main methods and achievements in motion planning and motion control for automated vehicles. The advantages and disadvantages of various planning and control methods are comparatively analyzed. Finally, some predictions and summaries based on the existing research results and trends are proposed. Through this analysis, it is believed that various types of algorithms will be further integrated in the future to complement each other’s strengths and weaknesses. The next area of research will be to establish more accurate vehicle models to describe vehicle motion, improve the generalization-solving ability of algorithms, and enhance the planning and control of integrated ‘human-vehicle-road’ traffic systems.

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Section: Artificial Potential Field Methodsmentioning

confidence: 99%

A Review of the Motion Planning and Control Methods for Automated Vehicles

Gao

Ding

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…However, the subjective nature of assigning weights to the factors in the objective function can lead to weight preferences. Some studies have proposed dynamically adjusting the weights of the objective function based on driving style types or the characteristics of natural driving data, which can enhance the adaptability of trajectories to drivers and driving scenarios [25,26]. Therefore, the trajectory cost function should have different forms in different environments, making it challenging to design a performance cost function with environmental adaptability.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Lane-Changing Trajectory Planning Method Based on the Least Action Principle

Liu,

Wen,

et al. 2023

Actuators

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This paper presents a hierarchical lane-changing trajectory planner based on the least action principle for autonomous driving. Our approach aims to achieve reliable real-time avoidance of static and moving obstacles in multi-vehicle interaction scenarios on structured urban roads. Unlike previous studies that rely on subjective weight allocation and single weighting methods, we propose a novel trajectory planning strategy that decomposes the process into two stages: candidate trajectory generation and optimal trajectory decision-making. The candidate trajectory generation employs a path-velocity decomposition method, using B-spline curves to generate a multi-objective optimal lane-changing candidate path. Collision checking eliminates paths at risk of collision with static obstacles. Dynamic programming (DP) and quadratic programming (QP) are then used to plan the velocity of safe paths, generating candidate lane-changing trajectories based on curvature checking. The optimal trajectory decision-making process follows the decision mechanism of excellent drivers. We introduce a comprehensive evaluation function, the average action, which considers safety, comfort, and efficiency based on the least action principle. Feasible trajectories are ranked based on their average action, and the trajectory with the minimum average action and no collision risk with moving obstacles is selected as the tracking target. The effectiveness of the proposed method is validated through two common lane-changing scenarios. The results demonstrate that our approach enables smooth, efficient, and safe lane-changing while effectively tracking the planned velocity and path. This method offers a solution to local trajectory planning problems in complex environments and holds promising prospects in the field of autonomous driving.

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A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience

Chen

Zhong

et al. 2023

Applied Sciences

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Existing environment modeling approaches and trajectory planning approaches for intelligent vehicles are difficult to adapt to multiple scenarios, as scenarios are diverse and changeable, which may lead to potential risks. This work proposes a cognitive spatial–time environment modeling approach for autonomous vehicles, which models a multi-scenario-adapted spatial–time environment model from a cognitive perspective and transforms the scenario-based trajectory planning problem into a unified spatial–time planning problem. The commonality of multiple typical Chinese road scenarios is analyzed, and a unified spatial–time environment model for multi-scenario adaptation is defined and established. The adaptability and trajectory planning potential of the spatial–time environment model are analyzed, and the planning results are obtained through a hybrid A* algorithm. The simulation results show that the proposal is effective in blurring the boundary between scenarios, allowing a single planning approach to adapt to multiple scenarios and plan optimal trajectories (optimal in both path and speed domains) and introducing more flexibility to the planning.

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Modelling Driver’s Behaviour While Avoiding Obstacles

Cited by 3 publications

References 63 publications

A Review of the Motion Planning and Control Methods for Automated Vehicles

A Review of the Motion Planning and Control Methods for Automated Vehicles

A Hierarchical Lane-Changing Trajectory Planning Method Based on the Least Action Principle

A Cognitive Environment Modeling Approach for Autonomous Vehicles: A Chinese Experience

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