Decision Making for Future Autonomous Vehicles in Complex Scenarios

高水平的车辆自主性承诺提高安全性和效率，需要高度准确的环境信息。实际上，由于信息不完整，该信息具有局限性，无论这是通过外感受或本体感受传感器误差，诸如噪声、丢失的数据包或遮挡，由此车辆可能阻碍传感器对场景的观察。这些限制为自动驾驶汽车带来了挑战，特别是在SAE 4级和5级不允许干预的情况下。本研究的使命是探索自动车辆决策背后的基本方法，当执行高度动态的机动，如超车单车道道路或合并到环岛或高速公路。超车即使对人类驾驶员来说也是一项挑战，但对于某些情况，如缓慢行驶的交通或高速公路施工作业，超车操纵对于及时到达目的地或防止因不作为而导致的进一步交通堵塞至关重要。传统的行为规划或决策系统使用基于规则的方法，通过对各个场景进行全面的系统工程，完成特定机动所需的车辆行为可以在车辆上的决策系统中明确定义。基于规则的方法的这种示例包括有限状态机或使用模糊逻辑的那些。例如，如果车辆在60 mph的道路上以40 mph的速度跟随单个领头车辆，则将指示其进行多个小的预定义操纵以安全且有效的方式超车;移动到相邻的迎面而来的车道中，加速以移动到领头车辆的前面，然后最终移动回到左手车道中并恢复正常驾驶。虽然基于规则的方法允许系统做出的决策的透明度，但它们需要对场景的广泛了解，而实际上由于道路使用者的数量和种类而永远不会相同。基于学习的方法试图通过使用模拟或真实世界的数据来训练能够生成正确策略的模型，从而涵盖一系列不同的场景。这种模型通常是黑箱，很难理解它在给定情况下的反应。这些方法包括强化学习或使用马尔可夫决策过程来制定系统的行为。本研究的重点是探索如何在任何给定的场景中引入部分可观测性，以用于计算自动驾驶所需的精确策略或动作-状态对。结果将提供洞察使用不确定性的策略生成沿着与描述如何使用来自其他自主控制子系统状态的信息可以被另外使用。

High level vehicle autonomy promises improved safety and efficiency requiring highly accurate information of the environment. In reality this information has limitations, owing to incomplete information, whether this is through exteroceptive or proprioceptive sensor errors such as noise, missing packets or occlusions whereby a vehicle may obstruct a sensors view of the scene. These limitations present a challenging situation for autonomous vehicles, particularly at SAE levels 4 and 5 whereby no intervention is permissible. This research mission is to explore the underlying methodologies behind automated vehicle decision making when executing highly dynamic manoeuvres such as overtaking on single carriageway roads or merging onto roundabouts or highways. Overtaking is challenging even for human drivers, yet for certain situations such as slow moving traffic or highway construction work, an overtaking manoeuvre is essential to arrive at a destination in a timely manner or preventing further traffic build up as a result of inaction.Traditional behaviour planning or decision-making systems use rule-based methods whereby through thorough systems engineering of individual scenarios, vehicle behaviours necessary to complete a specific manoeuvre can be explicitly defined in the decision-making system on the vehicle. Such examples of rule-based methodologies include finite state machines or those using fuzzy logic. If, for example, a vehicle was following a single lead vehicle at 40mph on 60mph road, it would be instructed to make a number of small predefined manoeuvres to overtake in a safe and efficient manner; move into the adjacent oncoming traffic lane, accelerate to move ahead of the lead vehicle then finally move back into the left hand lane and resume normal driving. While rule-based methods allow for transparency of the decisions being made by the system, they require extensive knowledge of the scenario which in practice is never the same due to the number and variety of road users.Learning based methods attempt to encompass a range of different scenarios by using either simulated or real-world data to train a model capable of generating the correct policy. This model is often black box and challenging to understand how it will react in a given situation. Such methods include reinforcement learning or using Markov Decision Process in formulating the behaviour of the system.The focus of this research is to explore how to incorporate partial observability in any given scenario can be used to compute accurate policies or action-state pairs required for automated driving. The outcome will provide insight toward using uncertainty for policy generation along with describing how using information from other autonomous control sub-systems states can be used additionally.