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.

Generalization of TORCS car racing controllers with artificial neural networks and linear regression analysis - ScienceDirect

Sohan Rudra - Research Associate - Technische Universität Darmstadt

Traffic Simulation and Autonomous Driving Experiment in VIPLE

A deep learning algorithm for simulating autonomous driving considering prior knowledge and temporal information - Chen - 2020 - Computer-Aided Civil and Infrastructure Engineering - Wiley Online Library

Adversarial Reinforcement Learning for Steering Cars from Virtual to Real World

CDS2K Dataset Papers With Code

TFix's Code Patches Data Dataset

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

Pseudo-code of dynamic scheduling framework with grouped whales (DSF.GW)

PDF) Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving

Machine Learning Datasets

International Journal of Communication Systems: Vol 33, No 11

PDF) Securing Future Autonomous & Connected Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward

Self Driving Cars, PDF, Cognitive Science

Matthew Muckley on LinkedIn: Announcing version 2.0 of the fastMRI dataset · Discussion #284 ·…