Driving video dataset github. For training ACO, you should also download label.

Driving video dataset github In addition to video recordings, sensor data from the smartphone as well as CAN bus data from the car are included. Video to JPG Converter - To extract frames from video. Contribute to lhyfst/awesome-autonomous-driving-datasets development by creating an account on GitHub. In other words, the goal of this paper is to achieve early localization of potentially risky traffic agents in driving videos. Tensorflow 2 implementation of complete pipeline for multiclass image semantic segmentation using UNet, SegNet and FCN32 architectures on Cambridge-driving Labeled Video Database (CamVid) dataset. 190–200. May 30, 2018 · As suggested in the name, our dataset consists of 100,000 videos. Welcome to Apolloscape's GitHub page! Apollo is a high performance, flexible architecture which accelerates the development, testing, and deployment of Autonomous Vehicles. @article{wang2019apolloscape, title={The apolloscape open dataset for autonomous driving and its application}, author={Wang, Peng and Huang, Xinyu and Cheng, Xinjing and Zhou, Dingfu and Geng, Qichuan and Yang, Ruigang}, journal={IEEE transactions on pattern analysis and machine intelligence}, year={2019}, publisher={IEEE} } Used convolutional neural networks (CNNs) to map the raw pixels from a front-facing camera to the steering commands for a self-driving car. B. Sign in A huge challenge for autonomous vehicles(ACs) is to have a dataset that captures real-world multitudinous driving conditions. It includes bounding boxes and tags for more than 10 object categories commonly encountered in urban settings. Dataset and Repository relied on these sources: Voigtlaender, Paul, et al. Oct 17, 2024 · However, these approaches remain constrained to 2D video generation, inherently lacking the spatiotemporal coherence required to capture intricacies of dynamic driving environments. Totally there are 45 tar. 1, pp. We present DrivingWorld (World Model for Autonomous Driving), a model that enables autoregressive video and ego state generation with high efficiency. 5 hours of web videos and synthesized data for diverse scene generation and advancing Sim2Real research. High-quality labels of disparity are produced by a model-guided filtering strategy from multi-frame LiDAR points. DrivingWorld formulates the future state prediction (ego state and visions) as a next-state autoregressive style. ApolloScape, part of the Apollo project for autonomous driving, is a research-oriented dataset and toolkit to foster JAAD dataset aims to provide samples for pedestrian detection, pedestrian action and gesture recognition, and behavioral studies of traffic participants. Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes, aggregated from multiple sources, all cropped spatio-temporally and filmed from a surveillance-camera like position. Find and fix vulnerabilities comma. This powerful end-to-end approach means that with minimum training data from humans, the system learns to steer, with or without lane markings, on both local video | blog; This repository contains the pytorch implementation for PPGeo in the paper Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling. See the full log list here. L. - GitHub - TRI-ML/DDAD: Dense Depth for Autonomous Driving (DDAD) dataset. To attack the task, we collected Berkeley DeepDrive Video Dataset with our partner Nexar, proposed a FCN+LSTM model and implement it using tensorflow. 2024-11-07: WorldDreamer V1. com Contribute to codemotozu/Self-Driving-Car-in-Video-Games-Documented development by creating an account on GitHub. The 10 classes to predict are: Safe driving JAAD dataset aims to provide samples for pedestrian detection, pedestrian action and gesture recognition, and behavioral studies of traffic participants. Dataset details: videos: has sub folders of different places/conditions which itself has clips of different streets. The Record011 and Record015 are used as the evaluation This project aims to detect and classify traffic objects in real-time using two advanced models: YOLO and Faster R-CNN. Extracted from Waymo self-driving vehicles, the data covers a wide variety of driving scenarios and Contact GitHub support about this user’s behavior. , frame) with question-answer pairs. PPGeo is a fully self-supervised driving policy pre-training framework to learn from unlabeled driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. Indian Driving Dataset Object Detection. com or message me through GitHub! IMPORTANT Absolutely, under NO circumstance, should one ever pilot a car using computer vision software trained on these datasets (or any home made software for that matter). Dataset ID MD-Auto-010; Dataset Name: Cloudy Day Crossroad Dash Cam Video Dataset: Data Type: Image: Volume: About 2. [huggingface] TL; DR MagicDriveDiT generates high-resolution and long videos for street-view with diverse 3D geometry control and multiview consistency. Using CoVLA, we investigate the driving capabilities of MLLMs that can handle vision, language, and action in a variety of driving scenarios. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. A image-based detection scheme alone cannot accurately detect the leading actions of the driver's behavior, such as the driver reaching for the phone; and ignoring the whole action will lead to a decrease in recognition accuracy. rar is the annotated shadow detection dataset. Annotation: Bounding Box,Tags: Annotation Notes # DRD (Dallas repeated driving cycle dataset) DRD: A repeated driving cycle dataset generated in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. This is a implementation of paper `End to End Learning for Self-Driving Cars' in 2016, I use two different datasets for driving dataset, three different structures of Pilotnet and several data augmentation methods. - GitHub - advaitsave/Multiclass-Semantic-Segmentation-CamVid: Tensorflow 2 implementation of complete pipeline for multiclass image semantic comma. , Chat2Scenario extracts driving scenarios from datasets using DeepLab is a state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e. @article{wang2024stag-1, title={Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model}, author={Wang, Lening and Zheng, Wenzhao and Du, Dalong and Zhang, Yunpeng and Ren, Yilong and Jiang, Han and Cui, Zhiyong and Yu, Haiyang and Zhou, Jie and Lu, Jiwen and Zhang, Shanghang}, journal={arXiv preprint arXiv:2412. CCD is distinguished from existing datasets for diversified accident annotations, including environmental attributes (day/night, snowy/rainy/good weather conditions), whether ego Autonomous driving datasets that are free to use commercially (MIT) - GitHub - klintan/av-datasets: Autonomous driving datasets that are free to use commercially (MIT) The current version of BROOK dataset has a total of 11 dimensional data, includng facial videos and many multi-modal/driving status data of 34 drivers. @inproceedings {fan2025depth, title = {Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video}, author = {Fan, Junkai and Wang, Kun and Yan, Zhiqiang and Chen, Xiang and Gao, Shangbing and Li, Jun and Yang, Jian}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, pages = {xxxxx--xxxxx}, year = {2025}} @inproceedings {fan2024driving, title [2024-11] The code is support finetuned Stable Video Diffusion on multiple driving dataset. Data source: YouTube, with careful collection and filtering process. [2024-9] Our paper is accepted by NeurIPS 2024 . The Driving-Thinking-Dataset is a unique collection of data gathered through a meticulous process combining naturalistic driving experiments and post-driving interviews. Extracting additional data fields: While YOLOv5 can currently extract data fields such as name, address, and date of birth, there may be other T. Video Upload: The user uploads a video file containing driving footage. DBNet is a large-scale driving behavior dataset, which provides large-scale high-quality point clouds scanned by Velodyne lasers, high-resolution videos recorded by dashboard cameras and standard drivers' behaviors (vehicle speed, steering angle) collected by real-time sensors. #Traffic Video Captioning Dataset (TVC-dataset) The existing datasets for video captioning contain videos with a variety of scenarios, and are not for ADAS traffic scenarios. The self-recorded driving videos required some pre-processing steps before it could be fed to the network. [CVPR 2024 Highlight] GenAD: Generalized Predictive Model for Autonomous Driving & Foundation Models in Autonomous System - DriveAGI/README. I'm attaching samples of the source images, the driving images I used, and the output Nov 7, 2024 · More than 100 million people use GitHub to discover, fork, and contribute to over 420 million projects. Thus, our released dataset consists of the following parts: The dataset consists of videos of drivers performing actions related to different driving scenarios in which it is intended to add monitoring systems, so driver state can be identified and later be able to estimate its risk on the road Jul 30, 2021 · The Waymo Open dataset is an open-source multimodal sensor dataset for autonomous driving. g. Find and fix vulnerabilities Users can upload a source image with single or multiple faces and driving video, then substitute the paths of source image and driving video for the source_image and driving_video parameters respectively and run the following command. Specifically, we utilize the world model An even more extensive survey from 02 Jan 2024 is A Survey on Autonomous Driving Datasets: Data Statistic, Annotation, and Outlook. pt and meta. In this paper, we introduce DriveDreamer4D, which enhances 4D driving scene representation leveraging world model priors. By capturing authentic interactions and Click to view [Feb 9, 2024] bdss named changed to rsud20k. MM-AU is the first large-scale dataset for multi-modal accident video understanding for safe driving perception. Each video is about 40 seconds long, 720p, and 30 fps. The training dataset is further split into 90:10 Training-Validation set. The corresponding RGB images and semantic labels can be found from the road01 part of Apolloscape dataset. python privacy ai robotics blur self-driving-car dataset-creation face-detection hide autonomous-driving privacy-protection anonymization driving-data license-plate-recognition blur-image indian-driving-dataset ethics-in-ai yolov8 We construct BDD100K, the largest open driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. To this end, we present a novel dataset called the R3 Driving Dataset, composed of driving data with different qualities. Instant dev environments comma. - awesome-video-anomaly-detection/README. It will generate a video file named result. Dolan, “Collecting highly parallel data for paraphrase evaluation,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 2011, vol. com LLM-Enhanced World Models for Driving Video Generation" (Zhao et al. Contribute to StephenAshmore/driving_video_dataset development by creating an account on GitHub. Each video has 40 seconds and a high resolution. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. e. the query, and (3) five-point scale saliency scores for all query-relevant clips There are a list of driver videos contained in dataset. D. shadow_dlake. We approach the task as a classification task. Following softwares were used, in sequence: Lossless Cut - To cut out relevant video without loss. For each frame, the system: Extracts the frame and resizes it to the required input size for the model. We provide some pretrained models that are ready to Full name: HDD HRI Driving Dataset Description: A large naturalistic driving dataset with driving footage, vehicle telemetry and annotations for vehicle actions and their justifications Data: scene video, vehicle data Annotations: bounding boxes, action labels video2dataset is designed such that you can chain together runs to re-process your downloaded data since webdataset is a valid input_format. The dataset consists of 10 videos clips of variable size recorded at 20 Hz with a camera mounted on the windshield of an Acura ILX 2016. 1104 is an End-To-End model. @article{yao2022dota, title={DoTA: unsupervised detection of traffic anomaly in driving videos}, author={Yao, Yu and Wang, Xizi and Xu, Mingze and Pu, Zelin and Wang, Yuchen and Atkins, Ella and Crandall, David}, journal={IEEE transactions on pattern analysis and machine intelligence}, year={2022}, publisher={IEEE} } [ECCV 2024] Official GitHub repository for "LingoQA: Visual Question Answering for Autonomous Driving", presenting the LingoQA benchmark, dataset and baseline model for autonomous driving Visual Question Answering (VQA). Task: large-scale video prediction for driving scenes. Generalized video prediction model facilitated with the largest driving video dataset (1700+ hours in OpenDV). For training ACO, you should also download label. " Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. The Driver Monitoring Dataset is the largest visual dataset for real driving actions, with footage from synchronized multiple cameras (body, face, hands) and multiple streams (RGB, Depth, IR) recorded in two scenarios (real car, driving simulator). The dataset consists of high-density images (≈10times more than the pioneering KITTI dataset), heavy occlusions, a large number of night-time frames (≈3times the scenes dataset), addressing the gaps in the existing datasets to push the boundaries of tasks in autonomous driving research to more challenging highly diverse environments. 1min of frames at 20 FPS. International Conference on Machine Vision and Information Technology (CMVIT), Sanya, China, February 2020. The corresponding paper can be found here. Predicts the driving behavior using the AWGRD model. These measurements are transformed Hi, When I provide an image instead of a driving video, I realize that the method is not able to rotate the head very well and often outputs artifacts. It has the following highlights: first multi-modal accident video understanding benchmark in the safe driving field. However, most conventional datasets only provide expert driving demonstrations, although some non-expert or uncommon driving behavior data are needed to implement a safety guaranteed autonomous driving platform. [2024-10] Our dataset DrivingDojo is released on Huggingface . Complete video list (under YouTube license): OpenDV Videos . The subset of the Dataset for AAAI-2020 paper: 《AutoRemover: Automatic Object Removal for Autonomous Driving Videos》 The Shadow Dataset. K. This repository contains a subset (3 recordings) of the SID - Stereo Image Dataset for Autonomous Driving in Adverse Conditions dataset recorded into video. May 4, 2019 · We construct a large-scale stereo dataset named DrivingStereo. It contains real traffic accident videos captured by dashcam mounted on driving vehicles, which is critical to developing safety-guaranteed self-driving systems. 1s interval. The videos also come with GPS/IMU information recorded by cell-phones to show rough driving trajectories. Anytime Video Converter - To resize video to reduce data. t. For example: A VQ-VAE [1,2] was used to heavily compress each video frame into 128 "tokens" of 10 bits each. Download the DrivingDojo dataset from the huggingface website: DrivingDojo. It contains over 180k images covering a diverse set of driving scenarios, which is hundreds of times larger than the KITTI stereo dataset. 2019. IDD consists of images, finely annotated with 16 classes collected from 182 drive sequences on Indian roads. In 2018 Yu et al. Each entry of the dataset is a "segment" of compressed driving video, i. Dec 27, 2023 · A New Dataset for Anomaly Detection in Driving Videos Yu Yao, Xizi Wang, Mingze Xu, Zelin Pu, Ella Atkins, David Crandall 💥 This repo contains the Detection of Traffic Anomaly (DoTA) dataset and the code of our paper . Related work: GenAD Accepted at CVPR 2024, Highlight Aug 18, 2024 · Our method effectively trains the video dehazing network using real-world hazy and clear videos without requiring strict alignment, resulting in high-quality results. An Android application is used to record smartphone sensor data, like accelerometer, linear acceleration, magnetometer and gyroscope, while a driver executed particular driving events. Displays the detected behavior on the video feed. MM-AU owns 11,727 in-the-wild ego-view accident videos. Within BDD Driving Project, we formulate the self driving task as future egomotion prediction. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. The data consists of 346 high-resolution video clips (5-15s) with annotations showing various situations typical for urban driving. The overall framework of our driving-video dehazing (DVD) comprising two crucial components: frame matching and video dehazing. Stratified splitting is used to split the dataset into 80:10 Training-Testing ratio. Host and manage packages Security. 🌟 SimGen addresses simulation to reality (Sim2Real) gaps via cascade diffusion paradigm, and follows layout guidance from simulators and cues of the rich text prompts to realistic driving scenarios. Jun 16, 2021 · A labeled dataset from a subset of the MVSEC dataset for car detection at night driving conditions. Chen and W. We construct BDD100K, the largest open driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. ai presents comma2k19, a dataset of over 33 hours of commute in California's 280 highway. The mini subset, OpenDV-mini, contains about 28 hours of videos Full name: HDD HRI Driving Dataset Description: A large naturalistic driving dataset with driving footage, vehicle telemetry and annotations for vehicle actions and their justifications Data: scene video, vehicle data Annotations: bounding boxes, action labels Aug 2, 2018 · comma. mp4 in the output folder, which is the animated video file. The videos comes with GPU/IMU data for Furthermore, we are able to fuse multiple videos through 3D point cloud registration, making it possible to inpaint a target video with multiple source videos. Captions for various open and constrained domain videos have been generated in the recent past but descriptions for driving dashcam videos have never been explored to the best of our knowledge. txt, and put them under {aco_path}/code and {your_dataset_directory}/ respectively. To this end, we present CARL-D, a large-scale dataset and benchmark suite for develop 2D object detection and instance/pixel-level segmentation methods for self-driving cars. In post-processing, we further enhance the cross-view consistency of subsequent frames and extend the video length by employing temporal sliding window algorithm. Contribute to AKASH2907/autonomous_driving_detection development by creating an account on GitHub. released BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the progress of image recognition algorithms on autonomous driving. py for training. In parallel to the videos we also recorded some measurements such as car's speed, acceleration, steering angle, GPS coordinates, gyroscope angles. Driver Drowsiness Dataset(D3S) The driver drowsiness datasets contains videos/frames of three subjects performing eyeclose, yawning, happy and neutral state of driver's infront of camera while driving. Here we provide the download and pre-processing instructions for the ROAD-Waymo dataset that is released through ROAD++ challenge: The Second Workshop & Challenge on Event Detection for Situation Awareness in Autonomous Driving and uses 3D-RetinaNet code as a baseline, which also contains the Surveillance Perspective Human Action Recognition Dataset: 7759 Videos from 14 Action Classes, aggregated from multiple sources, all cropped spatio-temporally and filmed from a surveillance-camera like position. It also reflects label distributions of road scenes significantly different from Dense Depth for Autonomous Driving (DDAD) dataset. MM-AU consists of two datasets, LOTVS-Cap and LOTVS-DADA. The dataset Researchers could work on creating new datasets that include driving licenses from other countries and regions and then train the YOLOv5 model on this data to improve its performance in those areas. E. To associate your repository with the video-datasets topic We now support evaluating driving performance of VAD. With 697K bounding boxes, 9K important object tracks, and 1-12 objects per video, IDD-X offers comprehensive ego-relative annotations for multiple important road objects covering 10 categories and 19 explanation label categories. Without any extra cost, our model can generate large-scale realistic multi-camera driving videos in complex urban scenes, fueling the downstream driving tasks. Yang. Our dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. GitHub: https://github. md at master · fjchange/awesome-video-anomaly-detection If you have a specific request or have an idea of how these datasets can be improved, email me at sullyfchen@gmail. Data preparation Inpainting dataset consists of synchronized Labeled image and LiDAR scanned point clouds. The videos are recorded using dashboard mounted cameras. May 18, 2019 · Contribute to 592McAvoy/paper-reading-3D-reconstruction development by creating an account on GitHub. We trained these models on the BDD100K dataset, a comprehensive driving video dataset with diverse scenes and annotations. Each scenario in the dataset We further convert the collected driving video to image sequences uniformly at a default frame rate, and annotate the actions performed in each driving scene (i. Resolution is over 1920 x 1080 and the number of frames per second of the video is over 30. Consequently, the environment perception system developed on these datasets cannot efficiently assist self-driving cars in traffic scenarios of sub-continent countries. The ONCE dataset is a large-scale autonomous driving dataset with 2D&3D object annotations. Another good source is the OpenDriveLab repository, where a dynamic list of datasets from the 06 Dec 2023 survey Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future is hosted DoTA can be considered an extention of A3D, which provides more videos (4677 raw videos) and annotations (anomaly types, anomaly objects, and tracking ids). , title = {v2e: From Video Frames to //github. , person, dog, cat and so on) to every pixel in the input image. Diversity Highlights: 1700 hours of driving videos, covering more than 244 cities in 40 countries. This dataset aims to delve into the cognitive processes and decision-making mechanisms of drivers in real-world driving scenarios. Papers for Video Anomaly Detection, released codes collection, Performance Comparision. Annotation: Bounding Box,Tags: Annotation Notes This paper introduces a new ”RoadText-1K” dataset for text in driving videos. Besides the wonderful papers we list below, we are very happy to announce that our group, NYU Learning Systems Laboratory, recently released a preprint titled: AD-L-JEPA: Self-Supervised Spatial World Models with Joint Embedding Predictive Architecture for Autonomous Driving with LiDAR Data, the first joint-embedding predictive architecture (JEPA) based spatial world models for self-supervised The objective of this project is to determine a riskiness score for all traffic agents within a driving scene. - GitHub - Maadaadata/Cloudy-Day-City-Road-Dash-Cam-Video-Dataset: The "Cloudy Day City Road Dash Cam Video Dataset" is crafted to address the challenges autonomous driving systems face in overcast weather conditions. 05280}, year={2024} } The largest open driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset contains coloured images of size 640 x 480 pixels which are resized to 64 X 64 coloured images for training and testing pusposes. We performed the experiment in 4 Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. Resolution is over 1920 x 1080 and the number of frames per second of the video is over 32. 4k annotated images: Data Collection: Driving Recorders Images. DoTA also provides more benchmarks in driving videos, such as anomaly detection, action recognition, and online action detection. Here's an example - with the WebVid data you downloaded in the previous example you can also run this script which will compute the optical flow for each video and store it in metadata shards (shards which only have the optical flow metadata in them). These datasets consist of driving scenes recorded in Austin, Texas using a dash-mounted smartphone. D. Behavior Monitoring: The system processes the video frame by frame. comma. The input of the model is a sequence of 5 images, each image has been recorded with a 0. DRD captures the GPS trajectories of a fixed driver using an internal combustion engine vehicle (Nissan, Altima 2012). Navigation Menu Toggle navigation. We randomly select some video that contains driver yawning and label the video fragment with 0-Normal, 1-Talking, 2-Yawning. r. This dataset tries to fill the gap with having stereo data for inference and testing purposes without having to download a large dataset. • 1 Million LiDAR frames, 7 Million camera images • 200 km² driving regions, 144 driving hours To fill this gap, we present IDD-X, a large-scale dual-view driving video dataset. Zhou, K. Training We provide main_label_moco. A Robust Monocular Depth Estimation Framework Based on Light-Weight ERF-PSPNet for Day-Night Driving Scenes. 2024-11-26: We have presented Video Autoregression Dreamer named DreamForge on arXiv. Wang, K. Due to size limitations, the videos are split across multiple repositories, such as DrivingDojo-Extra1, DrivingDojo-Extra2, and so on. 1 and the pretrained weight trained on nuScenes and nuPlan is released! We now support training and inference on nuScenes and nuPlan datasets. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. 📊 DIVA dataset comprises 147. The complete dataset OpenDV-YouTube is the largest driving video dataset to date, containing more than 1700 hours of real-world driving videos and being 300 times larger than the widely used nuScenes dataset. comma2k19 is a fully reproducible and scalable dataset. 🎦 The Largest Driving Video dataset to date, containing more than 1700 hours of real-world driving videos and being 300 times larger than the widely used nuScenes dataset. The dataset represents more than 1000 hours of driving experience with more than 100 million frames. over 10,000 YouTube videos, each video in the dataset is annotated with: (1) a human-written free-form NL query, (2) relevant moments in the video w. Demystifying interactions between driving behaviors and styles through self-clustering algorithms @inproceedings{HCI21/DBSCAN, title={{Demystifying The dataset is a collection of smartphone sensor measurements for driving events. "Mots: Multi-object tracking and segmentation. md at main · OpenDriveLab/DriveAGI In 2018 Yu et al. With the aim to explore dashcam video description generation for autonomous driving, this study presents DeepRide: a large-scale dashcam driving video . Dataset ID MD-Auto-011; Dataset Name: Low lighting Dash Cam Video Dataset: Data Type: Image: Volume: About 800 annotated images: Data Collection: Driving Recorders Images. gz files, each containing about 400 videos. The currently available video datasets are not annotated & most of them aren't high resolution videos which is again an impediment for object detection. ai for the people to experiment with. The rapid advancement of diffusion models has greatly improved video synthesis, especially in controllable video generation, which is essential for applications like autonomous driving. For the video understanding and captioning problem, we build a dataset for ADAS traffic scenarios, called Traffic Video Write better code with AI Security. an awesome list of autonomous driving datasets. Risky Object Localization (ROL) in a Driving Scene Dataset By Muhammad Monjurul Karim, Drew Racz, Gary Liu, Ziming Li, Yanguang Gong, Michael Incardona, William Li, Ruwen Qin, Zhaozheng Yin Download Find and fix vulnerabilities Codespaces. rgb gpcib lsqhkmke ynfq mwhcnd zvasj igm pqmkktg woss tguhnj bky gdjs gzr prjoaupp gxouzsp