A vehicle's lane-changing behavior is affected by the surrounding environment and driver factors, which makes it difficult to identify accurately. To solve this problem, a personalized lane-changing decision model based on a Long Short-term Memory (LSTM) network is proposed. First, an unsupervised clustering method is applied to recognize three distinct driving styles; Second, by considering the interactions among the target vehicle and surrounding vehicles, a benefit function is constructed to measure these interactions and generate the lane-changing benefit values. The lane-changing gain values and feature parameters are used as model inputs to construct a personalized lane-changing decision model using LSTM. Finally, the proposed method is validated with the NGSIM dataset: the overall accuracy of the model reaches 97.8% when considering different driving styles, which proves that the proposed method can achieve personalized lane-changing decisions based on different drivers' lane-changing behaviors.
Personalized lane change decision model based on long short-term memory network
Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024) ; 2024 ; Kuala Lumpur, Malaysia
Proc. SPIE ; 13184
2024-07-05
Conference paper
Electronic Resource
English
Lane change risk assessment and personalized lane change decision method
European Patent Office | 2023
|