V ̈olz, B., Behrendt, K., Mielenz, H., Gilitschenski, I., Siegwart, R., & Nieto, J. The fastest deformable part model for object detection. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems-CH’18. Communicating awareness and intent in autonomous vehicle-pedestrian interaction. Mahadevan, K., Somanath, S., & Sharlin, E. In this paper, we go through some of the challenges in Pedestrian and Autonomous vehicles interaction that autonomous vehicles might face while driving in an urban environment after that we propose a novel architecture for identifying pedestrian’s intention using pedestrian’s detection, pose estimation, and classification algorithms while discussing different methods of each. So, it is very important for self-driving cars to identify true intentions of on-road pedestrians and understand social interaction norms. Deciding whether a person standing on the sidewalk is about to cross the road, or they are just waiting near the sidewalk is a difficult task for an autonomous vehicle, and it could be a matter of life-and-death in case of a vehicle driving at very high speed. Social interaction is a complex task, there are uncountable scenarios that happen on roads that require human interaction both verbal and nonverbal.
To make autonomous vehicles fully autonomous, they require the ability to communicate with other road users (pedestrian, vehicles, and other road users) and understand their intentions. One of the biggest roadblocks for autonomous vehicles to reach full autonomy is driving in urban environments. Autonomous Vehicles are on rise around the globe, millions of them are already there on road with medium levels of automation but still there is a long way to go for full autonomy.
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