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：尽管典型的自动驾驶系统在感知精度、性能稳定性和响应敏捷性方面远超人类，但在运用创造力、适应性和韧性理解陌生环境的智慧方面，此类系统仍远逊于人类。当前自动驾驶大脑本质上是具备逻辑计算能力的专家系统，其思维流程类似于战术层面运作的左脑。为将自动驾驶车辆的安全性提升至下一代水平，需要引入能做出直观战略判断的右脑，以此监督战术行动规划。本文提出自动驾驶战略大脑（ADSB）概念：一种在更高抽象层级工作的场景感知与场景安全评估系统框架，融合经验引用、常识推理及目标价值判断能力，为自动驾驶规划中的决策提供情境视角。ADSB大脑架构由经验引用引擎（ERE）、常识推理引擎（CIE）及目标与价值保持器（GVK）组成。训练ERE模型使用了美国国家公路交通安全管理局（NHTSA）FARS/CRSS数据库中1975年至2018年期间的1,614,748个案例。CIE核心是基于ATOMIC的预训练模型COMET-BART，当战术级环境感知结论模糊时，该模型可提供方向性建议；同时，它还可利用未来场景模型提醒战术级决策系统对已感知的危险场景进行前瞻性规划。GVK能纳入任何额外由专家编写的定性规则。此外，我们相信ADSB方法具有良好的可扩展性，为解决基于规则的规划算法验证中遇到的长期尾案例问题提供了潜在方案。