Search Results for "artificial"
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Showing 10 open source projects for "artificial"
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highway-env
A minimalist environment for decision-making in autonomous driving
HighwayEnv is an OpenAI Gym-compatible environment focused on autonomous driving scenarios. It provides flexible simulations for testing decision-making algorithms in highway, intersection, and merging traffic situations. -
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CARLA Simulator
Open-source simulator for autonomous driving research.
CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites, environmental conditions, full control of all static and dynamic actors, maps generation and much more. Multiple clients... -
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AWS IoT FleetWise Edge
AWS IoT FleetWise Edge Agent
Easily collect, transform, and transfer vehicle data to the cloud in near-real-time. AWS IoT FleetWise makes it easy and cost-effective for automakers to collect, transform, and transfer vehicle data to the cloud in near-real-time and use it to build applications with analytics and machine learning that improve vehicle quality, safety, and autonomy. Train autonomous vehicles (AVs) and advanced driver assistance systems (ADAS) with camera data collected from a fleet of production vehicles.... -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV... -
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SVL Simulator
A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
LG Electronics America R&D Lab has developed an HDRP Unity-based multi-robot simulator for autonomous vehicle developers. We provide an out-of-the-box solution which can meet the needs of developers wishing to focus on testing their autonomous vehicle algorithms. It currently has integration with The Autoware Foundation's Autoware.auto and Baidu's Apollo platforms, can generate HD maps, and can be immediately used for testing and validation of a whole system with little need for custom... -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
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road-scene-understanding
A dataset for road scene understanding.
Autonomous driving is gaining increasing attention in the computer vision research community, as vision based scene understanding is key to self-driving cars. In this web page, we make image datasets public for the purpose of furthering research in scene understanding. -
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This project intends to develop a system to control the vehicles over a wireless network. Each vehicle contains embedded software that interfaces with a set of sensors and actuators that allow the vehicle to navigate, to communicate with roadside sensors,
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Alpamayo 2 Super
Open VLA model for autonomous driving reasoning and planning
Alpamayo2-Super is NVIDIA’s flagship open reasoning Vision-Language-Action (VLA) model for autonomous vehicle development, designed to accelerate Level 4 robotaxi and autonomous driving systems. Built on the NVIDIA Cosmos platform, it enables vehicles to perceive, reason, plan, and generate driving actions using human-like decision making rather than relying solely on predefined rules. The model processes multi-camera video, navigation signals, and driving context to produce driving...
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