Multi-Object Tracking and Trajectory Prediction. This repository contains all codes written for SUMMER RESEARCH INTERNSHIP (2021) at AI and Robotics Park (ARTPARK), IISc Bangalore
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Updated
Dec 1, 2022 - HTML
Multi-Object Tracking and Trajectory Prediction. This repository contains all codes written for SUMMER RESEARCH INTERNSHIP (2021) at AI and Robotics Park (ARTPARK), IISc Bangalore
Jekyll code for the website Computationally Thinking Blog
🌍 Discover advancements in 3D scene understanding with LiDAR techniques for semantic and panoptic segmentation, plus occupancy prediction.
Project on detecting lane lines on roads in a video stream, using polynomials
This is a Deep Learning Project to classify German Road Signs using deep neural networks and image processing.
[Small] Traffic sign classification using Tensorflow and LeNet.
Comparing the performance of MPC based racing and RL based racing
Computer vision project on vehicle detection and tracking on roads
A framework for the analysis of un(certainty) in traffic, using a crowdsourcing approach.
This repository hosts the Zenseact Open Dataset website.
🌐 A curated collection of large-scale 3D scene understanding models with real-world applications
CarND Term1 Traffic Sign Classifier
🌐 A curated collection of vision-language-action (VLA) models for autonomous driving applications
Inspired by comma.ai OpenPilot idea, this is an AD Autopilot on RaspberryPi based on ROS called RosPilot. The project currently performs lane follow feature. The codebase is written to be modular, enable quick prototyping and facilitate learning and collaboration across multiple users. The hardware used so far is the donkey car robocar + RPI 3
Lokale Navigation von Mikromobilitätsfahrzeugen mittels Reinforcement Learning
📐 Personal GitHub web page. Based on the minimal-mistakes Jekyll theme.
A framework for the analysis of trust in the interaction between pedestrians and vehicle (manual and automated), from the perspective of the driver of a manual or an automated vehicle, using a crowdsourcing approach.
Countdown for all* relevant conferences in the domain of autonomous driving
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