Show simple item record

dc.contributor.authorSana, Faizan 20:12:57 (GMT) 20:12:57 (GMT)
dc.description.abstractUnprotected left turns at unsignalized intersections, alongside pedestrians and adversarial vehicles, pose significant challenges for Autonomous Vehicle (AV)s. These challenges stem from the absence of traffic signals or signs, the dynamic nature of the environment shaped by human interactions at crosswalks, and the variability in intersection layouts. This thesis delves into addressing these challenges through the application of a hierarchical Deep Reinforcement Learning (DRL) approach, where the DRL policy governs high-level decision-making (or planning), and low-level Proportional-Integral-Derivative (PID) controllers handle actuation. To evaluate and train DRL policies, it was necessary to create a simulation environment within a high-fidelity environment with realistic behaviors and dynamic vehicle models. To the best of our knowledge, this research marks a pioneering effort in simulating pedestrian interactions within a high-fidelity environment, coexisting alongside adversarial vehicles within the CARLA simulation platform. We have dedicated extensive efforts to the development of this simulation, enabling straightforward customization of parameters such as the number of pedestrians, adversarial vehicles, and reward functions amongst others. This is made available open-source at intersection-carla-gym. The study evaluates five distinct model-free DRL algorithms, namely Deep Q-Learning (DQN), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Recurrent PPO, and Soft Actor Critic (SAC). The primary focus of this work is to conduct a comprehensive comparative analysis of these DRL algorithms within a hierarchical framework to enhance AV decision-making in complex and uncontrolled intersection scenarios. The training code, with its versatile software architecture is made available at Our findings reveal that Recurrent PPO, coupled with a discretized action space, outperforms the other algorithms, displaying the highest success rate and the lowest accident rate for executing unprotected left turns in chaotic intersections. This outcome underscores the potential of Recurrent PPO to navigate such complex traffic scenarios effectively. The thesis concludes by discussing potential extensions of the proposed hierarchical DRL system and outlining promising avenues for future research in the field of autonomous vehicle navigation at challenging and dynamic intersections.en
dc.publisherUniversity of Waterlooen
dc.relation.uri intersection-carla-gymen
dc.subjectautonomous drivingen
dc.subjectreinforcement learningen
dc.titleNavigating Unsignalized Intersections: Deep RL-Based Decision-Making and Control Framework for Autonomous Vehicles with Pedestrian Integrationen
dc.typeMaster Thesisen
dc.pendingfalse Design Engineeringen Design Engineeringen of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws.contributor.advisorLashgarian Azad, Nasser
uws.contributor.advisorRaahemifar, Kaamran
uws.contributor.affiliation1Faculty of Engineeringen

Files in this item


This item appears in the following Collection(s)

Show simple item record


University of Waterloo Library
200 University Avenue West
Waterloo, Ontario, Canada N2L 3G1
519 888 4883

All items in UWSpace are protected by copyright, with all rights reserved.

DSpace software

Service outages