Abstract
Different driving styles should be considered in path planning for autonomous vehicles that are travelling alongside other traditional vehicles in the same traffic scene. Based on the drivers’ characteristics and artificial potential field (APF), an improved local path planning algorithm is proposed in this paper. A large amount of driver data are collected through tests and classified by the K-means algorithm. A Keras neural network model is trained by using the above data. APF is combined with driver characteristic identification. The distances between the vehicle and obstacle are normalized. The repulsive potential field functions are designed according to different driver characteristics and road boundaries. The designed local path planning method can adapt to different surrounding manual driving vehicles. The proposed human-like decision path planning method is compared with the traditional APF planning method. Simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes are carried out. The simulation results show that the curves of human-like decision-making path planning method are more reasonable than those of the traditional APF path planning method; the proposed method can carry out more effective path planning for autonomous vehicles according to the different driving styles of surrounding manual vehicles.
Highlights
Autonomous vehicles can drive based on the perception of surrounding environmental conditions, just like human drivers [1]
The simulation tests of an individual driver and various drivers with different characteristics in overtaking scenes showed that the curves of human-like path planning method were more reasonable than those of the traditional artificial potential field (APF); the proposed method can make a more effective path plan for autonomous vehicles according to the different driving styles of surrounding manual vehicles
The deep learning method represented by the Keras neural network model was used in the path planning of autonomous vehicles, and the following conclusions were obtained: (1)
Summary
Autonomous vehicles can drive based on the perception of surrounding environmental conditions, just like human drivers [1]. A gap is considered admissible if it is possible to find a collision-free motion control that guides a robot through it, while respecting the vehicle constraints On this basis, a new navigation approach was developed, achieving an outstanding performance in unknown dense environments. In order to make the planned paths more in line with the actual road conditions, some researchers have integrated the driver model into the path planning algorithm to make the autonomous vehicles have human-like characteristics. The primary contributions of this paper lie in two aspects: (1) the driver characteristic identification algorithm based on the K-means clustering analysis algorithm and Keras neural network model can accurately classify the driving styles of different drivers; (2) an improved APF combined with driver characteristic identification is proposed.
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