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Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded.

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.