Deep learning enhanced driving behavior evaluation based on vehicle-edge-cloud architecture

With the rapid development of 5 G, artificial intelligence and other technologies, the intelligent transportation system (ITS) bursts flourish fireworks. It is acknowledged that driving security problem still runs through the ITS development history.It is the driver who plays the decisive role in a vehicle accident, and the performance of autopilot system is also the kernel in guaranteeing the security of autonomous vehicle. Therefore, many researchers devote to self-driving system optimization and human abnormal driving behavior detection. Note that they either relied on simulators, or confined to several specific driving patterns, which undoubtedly limited their application value. In addition, some works require the vehicles have high computing power and abundant storage memory, which aggravated their burden. Different from previous works, we propose a driving behavior evaluation scheme based on vehicle-edge-cloud architecture. When vehicles running on the road, they transmit the data reflecting the autopilots/driver behaviors to the edge networks via the telematics box. The edge networks use the driving behavior evaluation model trained by cloud server, and send the behavior rankings back to vehicles. The cloud server continuously trains and optimizes the driving behavior evaluation model using vehicle data, and regularly transmits the model to the edge networks for upgrading. The experimental results show robustness and feasibility of the scheme.