TOWARDS AUTONOMOUS DRIVING IN DENSE, HETEROGENEOUS, AND UNSTRUCTURED TRAFFIC

This dissertation addressed many key problems in autonomous driving towards handling dense, heterogeneous, and unstructured traffic environments. Autonomous vehicles (AV) at present are restricted to operating on smooth and well-marked roads, in sparse traffic, and among well-behaved drivers. We developed new techniques to perceive, predict, and plan among human drivers in traffic that is significantly denser in terms of number of traffic-agents, more heterogeneous in terms of size and dynamic constraints of traffic agents, and where many drivers do not follow the traffic rules. In this thesis, we present work along three themes—perception, driver behavior modeling, and planning. Our novel contributions include: 1. Improved tracking and trajectory prediction algorithms for dense and heterogeneous traffic using a combination of computer vision and deep learning techniques. 2. A novel behavior modeling approach using graph theory for characterizing human drivers as aggressive or conservative from their trajectories. 3. Behavior-driven planning and navigation algorithms in mixed (human driver and AV) and unstructured traffic environments using game theory and risk-aware control. Additionally, we have released a new traffic dataset, METEOR, which captures rare and interesting, multi-agent driving behaviors in India. These behaviors are grouped into traffic violations, atypical interactions, and diverse scenarios. We evaluate our perception work on tracking and trajectory prediction using standard autonomous driving datasets such as the Waymo Open Motion, Argoverse, NuScenes datasets, as well as public leaderboards where our tracking approach resulted in achieving rank 1 among over a 100 methods. We apply human driver behavior modeling in planning and navigation at unsignaled intersections and highways scenarios using state-of-the-art traffic simulators and show that our approach yields fewer collisions and deadlocks compared to methods based on deep reinforcement learning. We conclude the presentation with a discussion on future work