Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (1): 273-284.doi: 10.13229/j.cnki.jdxbgxb20210493

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Driver's lane change intention recognition in snow and ice environment based on neural network

Hui-qiu LU1(),Feng ZHAO1,Bo XIE1,Yan-tao TIAN1,2   

  1. 1.College of Communication Engineering，Jilin University，Changchun 130012，China
    2.Key Laboratory of Bionic Engineering，Ministry of Education，Jilin University，Changchun 130022，China
  • Received:2021-06-01 Online:2023-01-01 Published:2023-07-23

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Abstract:

The process of driving a car can be affected by many factors. Different drivers have different operating habits and different driving ways under different road conditions. Therefore， in this paper， driving characteristics in snow and ice covered environment were analyzed. Based on Carmaker， the hardware-in-the-loop driving simulation platform， data such as driver behavior parameters and vehicle parameters were collected under this condition （including the bifurcated road surface）， characteristic parameters were analyzed and selected， and data sample database was established. The intention recognition model was established based on neural network， and the model was verified in single condition and compound condition respectively. The performance of the model was compared and analyzed. It can be obtained that the model can accurately identify the driver's intention on the bifurcated road.

Key words: engineering of communication and transportation system, driver intention, neural network, driving simulation, the simulation analysis

CLC Number: 

  • U491.2

Fig.1

Man-machine cooperative system structure"

Fig.2

Simulation driving experimental platform"

Fig.3

Experimental roadmap"

Fig.4

Road environment and traffic flow"

Fig.5

Lane keeping and left-right lane changing conditions"

Fig.6

Carmaker parameter setting interface"

Fig.7

Comparison diagram A of vehicle parameters"

Fig.8

Comparison diagram B of vehicle parameters"

Fig.9

Speed change during lane change"

Fig.10

Boxplot of original distribution characteristics of driving state variable data"

Fig.11

Neural network topology"

Fig.12

Activation function"

Fig.13

Neural network structure of intention recognition model"

Fig.14

Driving intention recognition process"

Fig.15

Lateral displacement trajectories of vehicles on different road surfaces"

Fig.16

Result of intention model recognition with normal road data"

Fig.17

Result of intention model recognition with off road data"

Fig.18

Model recognition results of lane keeping condition"

Fig.19

Recognition results of left lane change working condition model"

Fig. 20

Model recognition results of right-lane change working conditions"

Fig.21

Recognition results of composite working condition model"

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