Open Source Windows Autonomous Driving Software

Autonomous Driving Software for Windows

View 5 business solutions

Browse free open source Autonomous Driving software and projects for Windows below. Use the toggles on the left to filter open source Autonomous Driving software by OS, license, language, programming language, and project status.

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  • 1
    CARLA Simulator

    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 in the same or in different nodes can control different actors. CARLA exposes a powerful API that allows users to control all aspects related to the simulation, including traffic generation, pedestrian behaviors, weathers, sensors, and much more. Users can configure diverse sensor suites including LIDARs, multiple cameras, depth sensors and GPS among others. Users can easily create their own maps following the OpenDrive standard via tools like RoadRunner.
    Downloads: 9 This Week
    Last Update:
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  • 2
    Software Infrastructure for Stanford's Autonomous Vehicles
    Downloads: 2 This Week
    Last Update:
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  • 3
    AWS IoT FleetWise Edge

    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. Improve electric vehicle (EV) battery range estimates with crowdsourced environmental data, such as weather and driving conditions, from nearby vehicles. Collect select data from nearby vehicles and use it to notify drivers of changing road conditions, such as lane closures or construction. Use near real-time data to proactively detect and mitigate fleet-wide quality issues.
    Downloads: 0 This Week
    Last Update:
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  • 4
    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,
    Downloads: 0 This Week
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  • 5
    BEVFormer

    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 queries. To aggregate spatial information, we design spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose temporal self-attention to recurrently fuse the history BEV information. Our approach achieves the new state-of-the-art 56.9\% in terms of NDS metric on the nuScenes \texttt{test} set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baseline.
    Downloads: 0 This Week
    Last Update:
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  • 6
    Coach

    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 to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning.
    Downloads: 0 This Week
    Last Update:
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  • 7
    SVL Simulator

    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 integrations. We hope to build a collaborative community among robotics and autonomous vehicle developers by open-sourcing our efforts. To use the simulator with Apollo 6.0 or master, first download the simulator binary, then follow our Running with latest Apollo docs. To use the simulator with Autoware.auto, first download the simulator binary, then follow the guide on our Autoware.auto.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 8
    highway-env

    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.
    Downloads: 0 This Week
    Last Update:
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  • 9
    road-scene-understanding

    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.
    Downloads: 0 This Week
    Last Update:
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