Deep Learning for Autonomous Vehicle Environment Understanding and Mapping
Keywords:
stabilizing, vehicle positionAbstract
Over the past several years, many startups and companies have been devoted to self-driving vehicles, such as Waymo, Zoox, Mobileye, Tesla, Uber, Drive.ai, and Aptivo. Both traditional automakers and emerging players have turned attention to this field. This intense interest is due to concerns about increasing pollution, city traffic congestion, and traffic safety. Automated vehicles could raise the efficiency of transportation systems and reduce the cost of drivers. The current development direction of automated vehicles is being made clear in the brand-new electronic control model: driving by wire. Breaking the traditional interaction mode with drivers, this new mode sets the automatic driving system as the core system of the vehicle, integrates into the vehicle system, and works to allow the vehicle to achieve the intelligence that is equivalent to that of a human driver. This automated system can use automatic control to make the vehicle achieve a series of driving tasks, including observing the surrounding environment, determining vehicle positions, planning the driving path, tracking the planned path, and stabilizing and adjusting the vehicle position, to achieve a series of requirements, including avoiding the vehicles and pedestrians and the driving process, for stopping and waiting when the scene becomes complex [1].
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