Deep Learning for Weather Condition Adaptation in Autonomous Vehicles
Keywords:
LiDARAbstract
Therefore, it is essential to include a variety of real-world scenarios (e.g. fog, rain, snow) in the design process to ensure that AVs are continuously able to safely maneuver and respond to different weather conditions. These different weather phenomena impact the way data are collected, processed, and post-processed, making it difficult for AVs to make timely and potentially life-saving decisions [1]. One way to address this issue is by developing deep learning techniques to enable AVs to be capable of automatically sensing and adapting to adverse conditions associated with different weather environments. This can be achieved by using LiDAR (Light Detection and Ranging) and plant control tools such as adaptive cruise control and torque control brake [2].
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References
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