Decision Making for Future Autonomous Vehicles in Complex Scenarios

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.