Recent years, autonomous driving has always been a hot topic, which is regarded as an important means to shape the future of transport. However, safety is always the essential of any auto-pilot systems in the real-world. The safety and security of autonomous vehicles faces big challenges in public acceptance and trust. Thus, being able to accurately perceive the surrounding environment and respond in real time is an important goal of autonomous driving at the decision-making level. In this paper, a lane change prediction model using convolutional neural network is proposed combining the features from space and time. The proposed CNN learns the behavior pattern from historical data and capable of making lane change predictions to assist the decision making of auto-pilot systems in real-time. The experiments are conducted on the Next Generation Simulation (NGSIM) dataset, and we are able to predict up to 7s horizon before the actual lane-change took place with the observations of one second. An accuracy of 99 % is achieved for a 3 second observation window without further feature engineering. Comparisons are performed with tree based and multi-layer perceptron based methods.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lane Change Intent Prediction Based on Multi-Channel CNN Considering Vehicle Time-Series Trajectory


    Contributors:
    Zhang, Yi (author) / Zhang, Sheng (author) / Luo, Ruikang (author)


    Publication date :

    2022-10-08


    Size :

    1111100 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    LITNT: A Target-Driven Trajectory Prediction Framework with Lane Change Intent Analysis

    Liu, Shuai / Zhao, Yiming / Wang, Zhen et al. | IEEE | 2024


    Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters

    Xuchuan Li / Lingkun Fan / Tao Chen et al. | DOAJ | 2021

    Free access

    Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters

    Li, Xuchuan / Fan, Lingkun / Chen, Tao et al. | BASE | 2021

    Free access

    Estimating Driver’s Lane-Change Intent Considering Driving Style and Contextual Traffic

    Li, Xiaohan / Wang, Wenshuo / Roetting, Matthias | IEEE | 2019