The rapid development of wireless technology has accelerated the development of Vehicular Ad Hoc Networks (VANETs) to support accident prevention and the safety of a vehicle driver. VANET is a form of Mobile Ad Hoc Network (MANET), and it differs from MANET in that the network topology of a VANET changes dynamically in response to the high mobility of a vehicle and the unstable link quality due to various types of road patterns. Since most access and merging conflicts occur due to vehicle movement patterns and traffic conditions, the collision rate can be reduced if each vehicle can predict the location, movement direction, and resource occupancy information of other vehicles. In this paper, we propose a collision avoidance method based on the vehicle mobility prediction model in TDMA-based VANET. The proposed algorithm allocates time-slots of TDMA to avoid access and merging collisions by predicting the mobility of nearby vehicles using control time-slot occupancy information, vehicle ID, hop information, vehicle movement direction, and longitude and latitude of a vehicle. Simulation results show that the proposed algorithm can reduce access and merging collision rates compared with other legacy algorithms, and the performance gain of the proposed algorithm is enhanced in road environments when traffic density is high and where vehicles have high mobility and change their travel directions frequently.
Human Movement Direction Prediction using Virtual Reality and Eye Tracking
