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Although a typical autopilot system far surpasses humans in term of sensingaccuracy, performance stability and response agility, such a system is stillfar behind humans in the wisdom of understanding an unfamiliar environment withcreativity, adaptivity and resiliency. Current AD brains are basically expertsystems featuring logical computations, which resemble the thinking flow of aleft brain working at tactical level. A right brain is needed to upgrade thesafety of automated driving vehicle onto next generation by making intuitivestrategical judgements that can supervise the tactical action planning. In thiswork, we present the concept of an Automated Driving Strategical Brain (ADSB):a framework of a scene perception and scene safety evaluation system that worksat a higher abstraction level, incorporating experience referencing,common-sense inferring and goal-and-value judging capabilities, to provide acontextual perspective for decision making within automated driving planning.The ADSB brain architecture is made up of the Experience Referencing Engine(ERE), the Common-sense Referencing Engine (CIE) and the Goal and Value Keeper(GVK). 1,614,748 cases from FARS/CRSS database of NHTSA in the period 1975 to2018 are used for the training of ERE model. The kernel of CIE is a trainedmodel, COMET-BART by ATOMIC, which can be used to provide directional advicewhen tactical-level environmental perception conclusions are ambiguous; it canalso use future scenario models to remind tactical-level decision systems toplan ahead of a perceived hazard scene. GVK can take in any additionalexpert-hand-written rules that are of qualitative nature. Moreover, we believethat with good scalability, the ADSB approach provides a potential solution tothe problem of long-tail corner cases encountered in the validation of arule-based planning algorithm.

Planning Automated Driving with Accident Experience Referencing and Common-sense Inferencing