UC San Diego

In highly dynamic urban environments, software stacks for autonomous driving applications must quickly adapt to fast changing environments. Examples of dynamic scenarios include construction sites, road closures, and lane level updates. Failure to adapt to changes in map definitions can result in catastrophic failures in the system that can lead to accidents or, at best, rule violations in shared public roads. This work focuses on identifying strategies that leverage automatically generated map representations to minimize human-in-the-loop efforts and explores methods for integrating nominal planners in the global planning task.

The first part of this dissertation covers multi-class semantic mapping for large scale urban driving applications. As part of this framework, sensor fusion based strategies are applied to provide robust depth and semantic estimates from the scene without making strong assumptions about the road topology. Secondly, rasterized and graphical representations are jointly leveraged to formulate a nominal global planning approach for lane-level navigation. This method utilizes the semantic maps introduced and employs a conditional generative model to explicitly model the multi-modal distribution of trajectories that are feasible when driving in an urban setting. We additionally provide details from real-world testing and the open-source data collected from the UC San Diego campus during 2020-2021. In the last chapter, 2D and 3D centerline prediction methods are introduced to reduce the gap in real-time scene understanding. This contribution outlines an automatic label generation process and additionally leverages an occlusion handling approach to reason about centerline prediction with varying degrees of occlusion. The methods proposed achieve robust performance in diverse driving scenarios with promising directions in autonomous driving architectures.