Autonomous vehicle requires a general planner for all possible scenarios. Existing researches design such a planner by a unified scenario description. However, it may significantly increase the planner complexity even in some simple tasks, e.g., car following, further resulting in unsatisfactory driving performance. This work aims to design a general planner which can 1) drive in all possible scenarios and 2) have lower complexity in some common scenarios. To this end, this work proposes a pertinent boundary for multi-scenario driving planning. The total approach is named as Pertinent Boundary-based Unified Decision system. Based on the original drivable area, the pertinent boundary can further support motion status and semantics of the traffic elements, which provides the potential of pertinent performance for given scenarios. The pertinent boundary can support unified driving with the drivable area, in the meantime, can be pertinently modified to support the pertinent driving decisions for identified driving scenarios (e.g., car-following, junction left turning). It will further avoid the bump between the connections of the scenarios due to the continuity of space boundary. Thus, the planner is suitable for the fully autonomous driving. The proposed method is validated in different classical driving decision scenarios. Results show that the proposed method can support pertinent driving decisions in identified scenarios, in the meantime, assure generalized cross-scenario planning when no scenario information is available. Such a method shed light on fully autonomous driving by pertinence improvement of multi-scenario decision in the complex real world.

From the early stage of robotic applications to current autonomous driving technologies, environment modeling has been acting as the middleware of connecting perception and decision layers. In robotic applications, space-oriented models (e.g., grid map, drivable area) are widely applied to faithfully reflect the space occupation. With the development of autonomous driving, highly dynamic and complex road environment brings rising need of understanding the type and motion status of objects, thus element list became the mainstream of environment model. However, along comes the reliablity problem caused by missed detection and irregular objects, which is still inevitable despite the detection accuracy improvement. In view of this, we provide a new view of driving environment with the proposed unified state-extended boundary (USEB), aiming to improve the reliablity of element-oriented model. For driving decision requirements, different types of elements are consistently converted into driving constraints. Semantics and dynamics are expressed as status of drivable area boundary, making it possible to merge space occupation to improve reliability against missed detection and irregular objects. Evaluation of USEB is carried out on nuScenes dataset. Comparative results show that the proposed USEB could cover the required information of driving decision, whereas achieving higher reliability than commonly applied element-oriented model.