openpilot
openpilot is open source software built to improve upon the existing driver assistance in most new cars on the road today. Tesla Autopilot like functionality for your Toyota, Honda, and other top brands. While engaged, openpilot includes camera based driver monitoring that works both day and night to alert the driver when their eyes are not on the road ahead.
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NVIDIA Alpamayo 2 Super
NVIDIA Alpamayo 2 Super is a frontier open model for robotaxis and autonomous vehicles, built to reason through rare, complex driving situations and produce decisions developers can inspect, validate, and trust. Based on NVIDIA Cosmos 3 Super Reasoner and post-trained with reinforcement learning, it combines commercial openness with multitask capabilities for autonomous driving. The model reasons across full-surround camera coverage, fusing front, side, and rear views to understand lane changes, merges, unprotected turns, and complex intersections. For each scenario, it can generate a planned vehicle trajectory, a chain-of-causation trace explaining the decision, a meta-action such as yielding or stopping, reasoning auto-labels for training and validation, and visual question-answering responses grounded in specific image regions. These linked outputs make it easier to connect what the model observed with the action it selected.
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Model Predictive Control Toolbox
Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multi-stage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver. You can evaluate controller performance in MATLAB® and Simulink by running closed-loop simulations. For automated driving, you can also use the provided MISRA C®- and ISO 26262-compliant blocks and examples to quickly get started with lane keep assist, path planning, path following, and adaptive cruise control applications. Design implicit, gain-scheduled, and adaptive MPC controllers that solve a quadratic programming (QP) problem. Generate an explicit MPC controller from an implicit design. Use discrete control set MPC for mixed-integer QP problems.
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Helm.ai
We license AI software throughout the L2-L4 autonomous driving stack, perception, intent modeling, path planning, and vehicle control. Highest accuracy perception and intent prediction, leading to safer autonomous driving systems. Unsupervised learning and mathematical modeling, instead of supervised learning, allow learning from huge datasets. Our technologies are up to several orders of magnitude more capital-efficient, enabling much lower cost of development. Helm.ai full scene vision-based semantic segmentation fused with Lidar SLAM output from Ouster. L2+ autonomous driving with Helm.ai across highways 280 to 92 to 101, lane-keeping + ACC lane changes. Helm.ai pedestrian segmentation, with key-point prediction. Pedestrian segmentation and keypoint detection. Rain lane detection corner cases and Lidar-vision fusion. Full scene semantic segmentation, botts dots, and faded lane markings.
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