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.

PDF] Study and Comparison of General Purpose Fuzzers

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

TORCS Dataset Papers With Code

Visual TORCS network controller pipeline. At each time-step a raw 64×64

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

PDF] Distributed Approach for implementation of A3C on TORCS

Anomaly Detection by ADGM / LVAE

PDF) Learning drivers for TORCS through imitation using supervised methods

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

Drones, Free Full-Text

B Ravi Kiran - CatalyzeX

Deep_Reinforcement_Learning_for_Autonomous_Driving_A_Survey

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