Decision Making for Autonomous Vehicles

When autonomous vehicles also use on-board units which are vehicular to everything communication modems, they become connected and autonomous vehicles (CAV) [1]. Due to their increasing availability, CAVs have been the focus of both academic and industry research for a while and, as a result, there are is lot of research on autonomous driving function controls [2], [3], [4], [5], [6], [7], [8], [9],;10], [11] and their higher level decision-making algorithms [12], [13], [14], [15]. Planning and decision making are the core functions for an autonomous vehicle for driving on the road safely and efficiently under different traffic scenarios. As discussed in [16], the decision making and planning algorithms for autonomous vehicles are aiming at solving significant problems in autonomous driving, like (a) determining the future path, (b) utilizing observations of the surrounding environment from the perception system, (c) acting properly when interacting with other road users, (d) instructing lowlevel controller of the vehicle and (e) ensuring autonomous driving is safe and efficient. Therefore, the planning and decision making affects the autonomous vehicle decisively. Depending on the traffic scenario, autonomous driving functions are designed for highway driving, off-road driving and urban driving. The research for highway driving and off-road driving have been going on for a long time and with many results on planning and decision making. Yet, due to the complexity of the urban traffic scenario, the decision making and planning for urban traffic environment has always been very challenging with many unsolved problems. The complexity of the urban traffic scenario is manifested in the following aspects which are discussed next.