[ECCV 2024 Oral] DriveLM: Driving with Graph Visual Question Answering
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Updated
Sep 24, 2024 - HTML
[ECCV 2024 Oral] DriveLM: Driving with Graph Visual Question Answering
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.
📐 Personal GitHub web page. Based on the minimal-mistakes Jekyll theme.
Comparing the performance of MPC based racing and RL based racing
End to End Autopilot Perception Playbook
This repository hosts the Zenseact Open Dataset website.
A framework for the analysis of un(certainty) in traffic, using a crowdsourcing approach.
Countdown for all* relevant conferences in the domain of autonomous driving
Lokale Navigation von Mikromobilitätsfahrzeugen mittels Reinforcement Learning
Jekyll code for the website Computationally Thinking Blog
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
My personal website
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
In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.
[Small] Traffic sign classification using Tensorflow and LeNet.
This is a Deep Learning Project to classify German Road Signs using deep neural networks and image processing.
Computer vision project on vehicle detection and tracking on roads
Project on detecting lane lines on roads in a video stream, using polynomials
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