As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario, and driving risk perception is based on the ICV technology. In this way, trajectories of surrounding vehicles can be predicted and tracked in a real-time manner. In this paper, vehicle dynamics based state-space equations conforming to model predictive controllers are set up to primarily explore and identify a safety domain of active collision avoidance. Furthermore, the model predictive controller is also designed and calibrated, thereby implementing the active collision avoidance strategy for vehicles based on the model predictive control method. At last, functional testing is conducted for the proposed active collision avoidance control strategy in a designed complex traffic scenario. The research findings here can effectively improve automatic driving, intelligent transportation efficiency and road traffic safety.

随着复杂的驾驶场景为将深度学习应用于安全驾驶提供了机会，基于深度学习的人工智能已成为高级驾驶辅助系统领域中的热门话题。本文着重分析在实际驾驶风险情况下智能互联车辆（ICV）的避碰的车辆主动安全控制，并且基于ICV技术的驾驶风险感知。通过这种方式，可以实时地预测和跟踪周围车辆的轨迹。在本文中，建立了符合模型预测控制器的基于车辆动力学的状态空间方程，以主要探索和识别主动避碰的安全域。此外，还对模型预测控制器进行了设计和校准，从而基于模型预测控制方法实现了车辆主动防撞策略。最后，在设计的复杂交通场景中，对所提出的主动防撞控制策略进行了功能测试。这里的研究成果可以有效地提高自动驾驶，智能交通效率和道路交通安全。