TECHNICAL FIELD

This invention relates generally to the autonomous vehicle field, and more specifically to a new and useful system and method for data-driven, modular decision making and trajectory generation in the autonomous vehicle field.

BACKGROUND

Making safe and effective decisions in an autonomous vehicle is a complex and difficult task. This type of decision-making requires understanding of the current environment around the vehicle, an understanding of how this environment will evolve in the future, along with other factors involved in achieving safe and continuous progress towards the predefined driving goal. All decisions have to be continuously constrained by both driving rules of the road and human driving conventions, which is a difficult problem even for humans at times, and therefore an exceptionally challenging problem to implement with autonomous vehicles. Both the complicated nature of the driving interactions and the immense number of possible interactions makes decision-making and trajectory generation a tremendously difficult problem for autonomous systems. Regardless of the complexity, autonomous vehicles are tasked with solving this problem continuously; thus, a feasible solution which ensures scalability along with the safety of all road users is essential.

Conventional systems and methods have approached this problem in one of two ways—programmed or learned. Programmed motion planners produce a set of rules and constraints hand tuned and optimized by experts. Examples of this include conventional decision tree architectures employing data-driven models, which have only been utilized in restricted capacities such as perception. Conventional programmed approaches suffer from numerous limitations, such as, but not limited to: the production of unnatural decisions and motions (e.g., as shown in the programmed trajectory inFIG. 5); an exhaustive list of scenarios to program; and others. In contrast, learned motion planners involve analyzing large amounts of human driving data and/or running driving simulations. Examples of this include holistic end-to-end systems and single monolithic networks to address an entire driving policy module (e.g., mid-to-mid systems). Learned approaches also suffer from numerous limitations and drawbacks, such as, but not limited to: lack of safety assurances (e.g., as a result of treating the problem of motion planning in an end-to-end fashion, traditional learning algorithms are not able to provide safety assurances regarding the trajectories created by their networks); sample sparsity (e.g., the ability to capture all possible samples [driving scenarios] that the vehicle will be encountering in the real world); a lack of interpretability and/or explainability; and others.

Thus, there is a need in the autonomous vehicle field to create an improved and useful system and method for decision making and trajectory generation.

