Although a typical autopilot system far surpasses humans in term of sensing accuracy, performance stability and response agility, such a system is still far behind humans in the wisdom of understanding an unfamiliar environment with creativity, adaptivity and resiliency. Current AD brains are basically expert systems featuring logical computations, which resemble the thinking flow of a left brain working at tactical level. A right brain is needed to upgrade the safety of automated driving vehicle onto next generation by making intuitive strategical judgements that can supervise the tactical action planning. In this work, we present the concept of an Automated Driving Strategical Brain (ADSB): a framework of a scene perception and scene safety evaluation system that works at a higher abstraction level, incorporating experience referencing, common-sense inferring and goal-and-value judging capabilities, to provide a contextual 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 to 2018 are used for the training of ERE model. The kernel of CIE is a trained model, COMET-BART by ATOMIC, which can be used to provide directional advice when tactical-level environmental perception conclusions are ambiguous; it can also use future scenario models to remind tactical-level decision systems to plan ahead of a perceived hazard scene. GVK can take in any additional expert-hand-written rules that are of qualitative nature. Moreover, we believe that with good scalability, the ADSB approach provides a potential solution to the problem of long-tail corner cases encountered in the validation of a rule-based planning algorithm.
翻译:虽然典型的自动驾驶系统在感知精度、性能稳定性和反应敏捷度方面远远超过了人类,但这种系统仍然远远落后于人类,因为了解一个具有创造性、适应性和弹性的不熟悉环境的智慧。当前的AD大脑基本上是专家系统,其逻辑计算方法类似于在战术一级工作的左大脑的思维流。需要有一个右大脑,通过作出直观的战略判断,将自动驾驶车的安全提升到下一代,从而可以监督战术行动规划。在这项工作中,我们提出了自动驾驶战略大脑(ADSB)的概念:一个场景感知和现场质量评估系统的框架,这个框架在更抽象的层次上发挥作用,包括参考经验、常识、目标和价值判断能力,为在自动驾驶规划中的决策提供背景视角。 ADSB大脑结构由经验参考引擎(ERE)、共同感知知识引擎(CIEFRS/CARSDSB) 向前方系统提供一个更深层次的预知和价值保存器(GVK) 。 FARS/CARSDSB 预估测测测测测度(FSB) 在1975年的SDSDSDSDSDSDA数据库中, 使用一个长期的智能数据数据库中,在使用SDISDIALDADIADADADDIDIDIDDIDDDDDDDDD数据库中提供一个长期数据数据库中提供一种长期的预判法。