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Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance. Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead.

The study done by the U.S. National Highway Traffic Safety Administration (NHTSA) shows that developing automotive collision warning and avoidance systems will be very effective in order to significantly reduce fatalities, injuries and associated costs. In order to develop an automotive collision warning and avoidance system, it will be necessary that the vehicles should be able to exchange (in real-time) their dynamic information such as speed, acceleration, direction, relative position, status of some devices like brake, steering wheel, gas pedal, etc. The only feasible way to exchange the vehicles’ dynamic information will be through the use of wireless communication technology. However, the wireless link setup time and communication latencies should be under certain bounds so that the vehicles can appropriately react on time to avoid collisions. This paper will present results from an experimental setup that simulates inter-vehicle communications.