TORCS Dataset Papers With Code

TORCS (The Open Racing Car Simulator) is a driving simulator. It is capable of simulating the essential elements of vehicular dynamics such as mass, rotational inertia, collision, mechanics of suspensions, links and differentials, friction and aerodynamics. Physics simulation is simplified and is carried out through Euler integration of differential equations at a temporal discretization level of 0.002 seconds. The rendering pipeline is lightweight and based on OpenGL that can be turned off for faster training. TORCS offers a large variety of tracks and cars as free assets. It also provides a number of programmed robot cars with different levels of performance that can be used to benchmark the performance of human players and software driving agents. TORCS was built with the goal of developing Artificial Intelligence for vehicular control and has been used extensively by the machine learning community ever since its inception.

Deep reinforcement learning for autonomous vehicles: lane keep and overtaking scenarios with collision avoidance

Paper Review: LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval – Andrey Lukyanenko

Machine Learning Datasets

CVPR 2018 Day 2 — notes. Day 2 of the CVPR conference was a…, by Erika Menezes

PDF] Distributed Approach for implementation of A3C on TORCS

Using Keras and Deep Deterministic Policy Gradient to play TORCS

International Journal of Communication Systems: Vol 33, No 11

BURST Dataset Papers With Code

Sensors, Free Full-Text

Service-Oriented Computing and System Integration: Software, IoT, Big Data, and AI as Services

Anomaly Detection by ADGM / LVAE

TFW: Annotated Thermal Faces in the Wild Dataset Dataset