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.

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

Performance after training across all environments for at most 2.5

NY Contract with MCI Worldcom 2003

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

A screenshot from TORCS. Download Scientific Diagram

Machine Learning Datasets

Virtual to Real Reinforcement Learning for Autonomous Driving

Graphical TORCS driver model and the corresponding training configurations.

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Using Keras and Deep Deterministic Policy Gradient to play TORCS

Neural Networks in Python: From Sklearn to PyTorch and Probabilistic Neural Networks

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