Research on Roadside Performance and Anti-Collision Algorithms in Vehicular Ad Hoc Networks (1-9)

With the continuous development and widespread adoption of Intelligent Transportation Systems (ITS), Vehicular Ad Hoc Networks (VANETs) have become a prominent network communication technology in this field with broad application prospects. VANETs represent a highly customized mobile ad hoc network that establishes vehicle-to-vehicle communication through wireless communication and data transmission technologies, connecting vehicles and roadside infrastructure to form a specialized dedicated network. The primary function enables all road users to obtain and transmit real-time traffic-related information to improve traffic efficiency, enhance road safety, and increase comfort.

In vehicular networks, particularly in urban environments, high-speed vehicle movement causes frequent changes in network topology, while uneven vehicle density distribution leads to frequent occurrences of sparse connectivity and local optima scenarios. Therefore, it is essential to design routing protocols specifically for VANETs that possess robustness, reliability, and real-time performance to ensure efficient network operation. These protocols typically implement algorithms like AODV (Ad hoc On-demand Distance Vector) or OLSR (Optimized Link State Routing) with modifications for vehicular dynamics, incorporating GPS data and movement prediction models.

This chapter addresses existing vehicle collision problems in urban environments by proposing a routing decision scheme based on geographical location and road traffic information provided by electronic navigation maps. The solution utilizes a simulated urban traffic network model where vehicle nodes are randomly distributed, conducting anti-collision research at intersections and simulating relative position warnings between roadside nodes. The implementation involves creating a city grid map using graph data structures, where nodes represent intersections and edges represent roads. Vehicle movement is simulated using kinematic equations with random speed variations, while collision detection algorithms employ bounding box intersection tests and time-to-collision calculations. This approach generates urban traffic information network results under different simulations, providing guidance for regional traffic communication design and road condition information improvement.