Search Results for "driving"
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Showing 30 open source projects for "driving"
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1
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. -
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DriveLM
Driving with Graph Visual Question Answering
...The system includes DriveLM-Data, a dataset built on driving environments such as nuScenes and CARLA, where human-written reasoning steps connect different layers of driving tasks. This design allows models to learn relationships between objects, behaviors, and navigation decisions through graph-structured logic. -
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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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AWS IoT FleetWise Edge
AWS IoT FleetWise Edge Agent
...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. -
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Open Interface
Control Any Computer Using LLMs
...By sending user requests to an LLM backend, it determines the necessary steps and executes them by simulating keyboard and mouse inputs. The system can adjust its actions based on real-time feedback, providing a self-driving computer experience. -
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CatBoost
High-performance library for gradient boosting on decision trees
...It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. CatBoost was developed by Yandex and is used in various areas including search, self-driving cars, personal assistance, weather prediction and more. -
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Auto Claude
Autonomous multi-session AI coding
...The project aims to make “agentic software engineering” feel like running a small virtual dev team by giving you an opinionated process for turning goals into scoped tasks and then driving those tasks to completion. It includes guardrails intended to keep automation safer, such as restricting file operations to the project workspace and controlling which commands can be run based on the detected tech stack. -
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pix2pixHD
Synthesizing and manipulating 2048x1024 images with conditional GANs
pix2pixHD is a PyTorch-based implementation of a conditional generative adversarial network designed for high-resolution image-to-image translation, capable of producing photorealistic outputs at resolutions up to 2048×1024. It is widely used to convert structured inputs such as semantic label maps into realistic images, making it particularly valuable in applications like autonomous driving simulation, face synthesis, and scene generation. The model improves upon earlier GAN approaches by introducing multi-scale generators and discriminators that enable stable training and fine detail generation at large resolutions. It also supports interactive editing, allowing users to modify semantic regions and regenerate images with realistic adjustments. -
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CVPR 2026
Collection of CVPR 2026 Papers and Open Source Projects
...The repository acts as a continuously updated catalog of cutting-edge research across a wide range of topics including computer vision, multimodal AI, generative models, diffusion systems, autonomous driving, medical imaging, and remote sensing. Each entry typically links to the research paper as well as the public code repository associated with the work, allowing researchers and developers to quickly access reproducible implementations. The project serves as a centralized index that makes it easier for practitioners to explore the latest advances presented at major computer vision conferences. ... -
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10
Inkeep
Create AI Agents in a No-Code Visual Builder or TypeScript SDK
...Agents built with this framework can act as real-time conversational assistants — for example, handling help desk inquiries, providing internal support to teams, or driving in-app experiences — and they can be extended to automate multi-step tasks that interact with external systems like CRMs, knowledge bases, or ticketing systems. The project includes support for designing rich workflows where agents communicate with each other (agent-to-agent communication) and maintain state. -
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Adaptive Intelligence
Adaptive Intelligence also known as "Artificial General Intelligence"
Adaptive Intelligence is the implementation of neural science, forensic psychology , behavioral science with machine-learning and artificial intelligence to provide advanced automated software platforms with the ability to adjust and thrive in dynamic environments by combining cognitive flexibility, emotional regulation, resilience, and practical problem-solving skills. -
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MentDB Projects
Generalized Interoperability and Strong AI
MentDB is an open-source platform driving research into next-generation AI and universal data exchange. Our architecture is built around the revolutionary Mentalese Query Language (MQL). MentDB Weak (Generalized Interoperability): A unified data layer enabling seamless data exchange and application integration (SOA, ETL, Data Quality). We eliminate data silos through a single, generalized data language. -
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Thinknowlogy
The world's only naturally intelligent knowledge technology
...Natural language and spatial information are sources of natural intelligence: - Natural language is providing concrete logic for organizing knowledge objects, - Spatial information provides concrete logic for organizing spatial objects (utilized in, e.g., self-driving cars). In this way, our brains know how to organize their knowledge and spatial information. I focus on natural language because this source of natural intelligence is hardly understood by scientists. Hence, the inability of Large Language Models to organize changes in their knowledge independently. -
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VectorDB
A Python vector database you just need, no more, no less
...It's a testament to effective Pythonic design without over-engineering, making it a lean yet powerful solution for all your needs. vectordb capitalizes on the powerful retrieval prowess of DocArray and the scalability, reliability, and serving capabilities of Jina. Here's the magic: DocArray serves as the engine driving vector search logic, while Jina guarantees efficient and scalable index serving. This synergy culminates in a robust, yet user-friendly vector database experience, that's vectordb for you. -
15
YoloV3 Implemented in TensorFlow 2.0
YoloV3 Implemented in Tensorflow 2.0
...The project supports both pretrained models and full training pipelines, enabling researchers and developers to adapt YOLOv3 for tasks such as surveillance, robotics, autonomous driving, and image analysis. -
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ThoughtSource
A central, open resource for data and tools
ThoughtSource is a central, open resource and community centered on data and tools for chain-of-thought reasoning in large language models (Wei 2022). Our long-term goal is to enable trustworthy and robust reasoning in advanced AI systems for driving scientific research and medical practice. -
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AB3DMOT
Official Python Implementation for "3D Multi-Object Tracking
AB3DMOT is a real-time 3D multi-object tracking framework designed for applications such as autonomous driving and robotics perception. The system processes detection results from 3D object detectors that analyze LiDAR point clouds and uses them to track multiple objects across consecutive frames. Its tracking pipeline relies on a combination of classical algorithms, including a Kalman filter for state estimation and the Hungarian algorithm for data association between detected objects and existing tracks. ... -
18
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. ... -
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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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neurojs
A JavaScript deep learning and reinforcement learning library
...Several interactive demonstrations included with the project illustrate how neural networks can be used to train agents in simulated tasks, including a browser-based self-driving car example. These demos allow users to visualize how reinforcement learning agents improve their behavior over time as they receive rewards and update their neural networks. -
21
BNFGen
Generates random text based on context-free grammars defined in BNF
...Project goals are to make it easy to write and share grammar and give the user total control of and insight into the generation process. BNFGen provides a "DSL" for grammar definitions. It's a familiar BNF-like syntax with a few additions. One problem with using straight BNF for driving language generators is that you have no control over the process. BNFGen adds two features to fix that. The canonical way to express repetition in BNF is to use a self-referential recursive rule. In classic BNF, that can easily lead to the process terminating to early, since there's a 50% chance that it will take the non-recursive alternative. -
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Semantic Segmentation Editor
Web labeling tool for bitmap images and point clouds
A web-based labeling tool for creating AI training data sets (2D and 3D). The tool has been developed in the context of autonomous driving research. It supports images (.jpg or .png) and point clouds (.pcd). It is a Meteor app developed with React, Paper.js, and three.js. -
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Coach
Enables easy experimentation with state of the art algorithms
...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. ... -
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DeepTraffic
DeepTraffic is a deep reinforcement learning competition
DeepTraffic is a deep reinforcement learning simulation designed to teach and evaluate autonomous driving algorithms in a dense highway environment. The system presents a simulated multi-lane highway where an AI-controlled vehicle must navigate traffic while maximizing speed and avoiding collisions. Participants design neural network policies that determine the vehicle’s actions, such as accelerating, decelerating, changing lanes, or maintaining speed. -
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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.
