Autonomous Driving: Technical, Legal and Social Aspects
by Andreas Herrmann, et al.A comprehensive exploration of the technical and societal implications of autonomous vehicles, essential for understanding the broader context of your simulation.
Deep Learning for Computer Vision with Python
by Adrian RosebrockThis book provides practical insights into computer vision techniques, crucial for implementing effective perception systems in your simulation.
Reinforcement Learning: An Introduction
by Richard S. Sutton, Andrew G. BartoA foundational text on reinforcement learning that guides you through theories and applications vital for navigation tasks in autonomous systems.
Computer Vision: Algorithms and Applications
by Richard SzeliskiAn essential resource for understanding key computer vision algorithms, directly applicable to enhancing your simulation's perception capabilities.
Ethics of Autonomous Vehicles
by Patrick Lin, et al.An insightful examination of ethical dilemmas in autonomous driving, helping you navigate the moral landscape of your simulation project.
Probabilistic Robotics
by Sebastian Thrun, Wolfram Burgard, Dieter FoxThis book covers probabilistic approaches essential for sensor fusion, enhancing the reliability of your autonomous driving simulation.
Artificial Intelligence: A Guide to Intelligent Systems
by Michael NegnevitskyA broad overview of AI systems, providing foundational knowledge that underpins the technologies used in autonomous driving simulations.
Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library
by Gary Bradski, Adrian KaehlerA hands-on guide to computer vision using OpenCV, perfect for practical implementations in your simulation project.