協調型アダプティブクルーズコントロールにおける車線変更予測モデル
A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems
V2V通信で周囲車両の情報を共有するCACCシステムにおいて、LSTMを用いてドライバーの車線変更意図を予測し、ACCと比較して精度が大幅に向上することを示した。
著者: Armin Nejadhossein Qasemabadi, Saeed Mozaffari, Mahdi Rezaei, Majid Ahmadi, Shahpour Alirezaee
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional modules in advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle communication (V2V), vehicles can share traffic information with surrounding vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies on the vehicle's sensors to obtain the position and velocity of the leading vehicle, CACC also has access to the acceleration of multiple vehicles through V2V communication. This paper compares the type of information (position, velocity, acceleration) and the number of surrounding vehicles for driver lane change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD dataset to predict lane change intention. Results indicate a significant improvement in accuracy with an increase in the number of surrounding vehicles and the information received from them. Specifically, the proposed model can predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and CACC scenarios, respectively.
