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.

翻译：精确的换道预测可减少潜在事故风险并提升道路交通安全。自适应巡航控制（ACC）、车道偏离避免（LDA）及车道保持辅助（LKA）是先进驾驶辅助系统（ADAS）的典型功能模块。得益于车车通信（V2V）技术，车辆可与周边车辆共享交通信息，从而支持协同自适应巡航控制（CACC）。相较于ACC仅依赖车辆传感器获取前车位置与速度，CACC还可通过V2V通信获取多辆车的加速度信息。本文对比了信息类型（位置、速度、加速度）及周围车辆数量对驾驶员换道预测的影响。我们基于HighD数据集训练了长短期记忆网络（LSTM）以预测换道意图。结果表明，随着周围车辆数量增加及获取信息丰富度的提升，预测精度显著提高。具体而言，所提模型在ACC和CACC场景下对自车换道的预测准确率分别达到59.15%和92.43%。