Self-driving vehicles have long been envisioned as a massive leap forward in transportation technology. Although several efforts to developing fully autonomous vehicles are currently being undertaken in both industry and academia, so far none have achieved the promise of full self-driving. Of the challenges in building the autonomous software for self-driving cars, one of the most prominent is perception, or the ability for the vehicle to sense the world around it. To meet the requirements for practical deployment onto autonomous vehicles, perception systems must meet four key metrics of efficiency: accuracy, low latency, reasonable compute hardware, and training data efficiency.
In this dissertation, we will introduce novel approaches to AV perception while aiming to address the four metrics for efficiency. We introduce four major new perception schemes during the course of this dissertation.