In the fields of advanced driver assistance systems (ADAS) and Internet of Vehicles (IoV), predicting the vehicle state is essential, including the ego vehicle’s position, velocity, and acceleration. In ADAS, an early position prediction helps to avoid traffic accidents. In IoV, the vehicle state prediction is essential for the required calculation of the expected reliable communication time between two vehicles. Many approaches have emerged to perform this vehicle state prediction. However, such approaches consider limited information of the ego vehicle and its surroundings, and they may not be very effective in practice because the real situation is highly complex and complicated. Moreover, some of the approaches often lead to a delayed prediction time due to collecting and calculating the substantial history information. By assuming that the driver is a robot driver, which eliminates distinct driving behaviors of different persons when facing the same situation, this paper creates a decision tree as a new quick and reliable method adapted to all road segments, and it proposes a new method to perform the vehicle state prediction based on this decision tree.
Self-adaptive power control with deep reinforcement learning for millimeter-wave Internet-of-vehicles video caching