![]() ![]() Considering environmental interference, candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results. Second, the optimized candidate regions of interest are classified with confidence levels by ShuffleNet. ![]() ![]() First, a road feature extraction model based on multi-task learning is conducted, which can simultaneously segment the drivable area and road cast shadow. Therefore, this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will experience. However, most existing methods are solely based on a dynamics-based method or an image-based method, which is susceptible to road excitation limitations and interference from the external environment. Monitoring the type of RSC is essential for both transportation agencies and individual drivers. The type of road surface condition (RSC) will directly affect the driving performance of vehicles. ![]()
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