Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

Wang, Shaobo; Lin, Fanghua; Wang, Tiancheng; Zhao, Youqun; Zang, Liguo; Deng, Yaoji

doi:10.3390/act11020052

Cited by 16 publications

(11 citation statements)

References 40 publications

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“…By using a Python API, it is possible to select maps, change the weather, spawn various vehicles, and design vehicular control algorithms. Additionally, we have introduced Logitech G29's virtual driving hardware [43] to observe specific vehicle operations visually. Nevertheless, determining an optimal set of parameters poses a significant challenge, and the remaining sections of this paper are dedicated to addressing this issue.…”

Section: Simulation Platformmentioning

confidence: 99%

“…By using a Python API, it is possible to select maps, change the weather, spawn various vehicles, and design vehicular control algorithms. Additionally, we have introduced Logitech G29's virtual driving hardware [43] to observe specific vehicle operations visually. In this paper, CARLA version 0.9.13 is utilized, and Figure 4b illustrates the AV pathtracking flow chart in our simulation environment.…”

Section: Simulation Platformmentioning

confidence: 99%

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A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

Chen,

2023

Mathematics

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Autonomous driving technology heavily depends on accurate and smooth path tracking. Facing complex urban driving scenarios, developing a suite of high-performance and robust parameters for controllers becomes imperative. This paper proposes a stochastic simulation-based optimization model for optimizing the Proportional–Integral–Differential (PID) controller parameters, with tracking accuracy and smoothness as bi-objectives, and solves it using a domination-measure-based efficient global optimization (DMEGO) algorithm. In this model, the tracking accuracy and smoothness are indexed by the normalized dynamic time warping (NDTW) and the mean absolute lateral acceleration (MALA), respectively. In addition, we execute the PID controller in a realistic simulation environment using a CARLA simulator, which consider various city scenes, diverse routes, different vehicle types, road slopes, etc., to provide a comprehensive and reliable evaluation for the designed PID controller. In the DMEGO method, each solution undergoes evaluation using a fixed number of costly simulations. Then, utilizing the solutions and their estimated bi-objective values, two surrogate models for the bi-objectives are constructed using the Gaussian process (GP) model. The preliminary nondominated solutions can be obtained by optimizing the two surrogate models. Finally, a novel performance metric known as the domination measure is employed to evaluate the quality of each solution. This metric is then integrated with the crowding distance to selectively retain a candidate solution exhibiting superior performance and good diversity for the next iteration. In our numerical experiments, we first test the DMEGO algorithm against three other counterparts using a stochastic FON benchmark. The proposed approach is then employed to optimize the PID parameters considering the complexity and uncertainty of urban traffic. The numerical results demonstrate that the nondominated solutions obtained by DMEGO exhibit excellent performance in terms of tracking accuracy and smoothness under limited simulation budgets. Overall, the proposed approach may be a viable tool for solving multi-objective simulation-based optimization problem under uncertainties.

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Section: Simulation Platformmentioning

confidence: 99%

“…By using a Python API, it is possible to select maps, change the weather, spawn various vehicles, and design vehicular control algorithms. Additionally, we have introduced Logitech G29's virtual driving hardware [43] to observe specific vehicle operations visually. In this paper, CARLA version 0.9.13 is utilized, and Figure 4b illustrates the AV pathtracking flow chart in our simulation environment.…”

Section: Simulation Platformmentioning

confidence: 99%

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

Chen,

2023

Mathematics

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“…So, the effectiveness of vehicle path planning algorithms can be verified through different experimental scenarios. Road scenes can be viewed from different perspectives, including different obstacle environments [21], dynamic obstacle environments [22], constant-speed and variable-speed obstacle vehicle environments [23], and different manual-driving vehicle environments [24]. The artificial potential field algorithm can be improved from various perspectives to adapt to different road scenarios.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

Zhai,

Pei

2023

Sustainability

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The crucial dynamic path planning of autonomous vehicles is achieved via obstacle avoidance path planning technology. The reduction of the tire adhesion coefficient on icy and snowy roads (ISRs) increases the difficulty of autonomous vehicles’ control. In this paper, the driving characteristics of vehicles on ISRs are established, and the artificial potential field function is introduced to avoid collision risk when planning a path. A dynamic path planning algorithm for autonomous vehicles based on the artificial potential field (APF) is established. The adjustment factor is added to the gravitational potential field, and a judgment coefficient is added to the repulsive potential field to improve the artificial potential field function, based on the low adhesion of vehicles on ISRs. Moreover, a path with a continuous curvature is generated to achieve the driving comfort and driving safety of the planned path via trajectory smoothing. By establishing the Carsim/Simulink co-simulation platform, the effectiveness of dynamic path planning for autonomous vehicles under different algorithms and different obstacle models is compared. The results show that the improved APF algorithm has an obvious effect on the smoothness of the path and the reduction of the curvature mutation and can generate a safe and efficient path on icy and snowy roads. The dynamic obstacle avoidance of the improved APF algorithm improves the pre-judgment accuracy of the collision risk assessment of autonomous vehicles and shows the superiority of the improved algorithm.

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“…Trajectory planning is a key technology to realize automated vehicle driving, which can solve the problem of how the car should go while satisfying traffic rules and obstacle avoidance constraints. The trajectory planning problem for autonomous semi-trailer trains is more challenging than that of ordinary single-unit vehicles [2,3]. On the one hand, the semi-trailer train has a flexible connection between the tractor and the semi-trailer, and the obstacle avoidance constraints are difficult to handle.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

Wang,

Li,

Liu

et al. 2023

Applied Sciences

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With the development of science and technology, self-driving technology is gradually being applied to automobile semi-trailer trains. Aiming at the problem that it is challenging to plan the traveling trajectory of an autonomous semi-trailer train, a trajectory planning algorithm based on a constrained iterative linear quadratic regulator is proposed. The constrained iterative linear quadratic regulator solves the problem that the iterative constrained linear quadratic regulator cannot deal with inequality constraints by transforming inequality constraints into interior point penalty functions to deal with all kinds of inequality constraints in the trajectory planning problem. When dealing with the obstacle avoidance problem, the body contour is modeled approximately according to the actual shape of the vehicle, and the obstacle avoidance constraints are transformed into inequality constraints. Considering the unique body structure of a semi-trailer train, the articulation angle constraint function is designed according to different vehicle speeds. Simulation experimental results show that the algorithm can plan safe driving trajectories for semi-trailer trains in complex traffic scenarios.

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Autonomous Vehicle Path Planning Based on Driver Characteristics Identification and Improved Artificial Potential Field

Cited by 16 publications

References 40 publications

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application

The Dynamic Path Planning of Autonomous Vehicles on Icy and Snowy Roads Based on an Improved Artificial Potential Field

Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR

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