ROAD: The Road Event Awareness Dataset for Autonomous Driving

Singh, Gurkirt; Akrigg, Stephen; Maio, Manuele Di; Fontana, Valentina; Alitappeh, Reza Javanmard; Saha, Suman; Saravi, Kossar Jeddi; Yousefi, Farzad; Culley, Jacob; Nicholson, Tom; Omokeowa, Jordan; Khan, Salman; Grazioso, Stanislao; Bradley, Andrew P.; Gironimo, Giuseppe Di; Cuzzolin, Fabio

doi:10.1109/tpami.2022.3150906

Cited by 40 publications

(23 citation statements)

References 87 publications

(164 reference statements)

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“…Among the datasets dedicated to autonomous vehicles, the vast majority are for autonomous driving in road environments and focus on object detection and scene segmentation. We can divide them into two main families [ 3 , 24 , 25 ]: (1) Datasets without ground truth data (images as a single modality), and (2) datasets with ground truth data (as a multimodality). Some of them are mono-modal (e.g., based on camera as single modality), and others are multi-modal (e.g., multisensor fusion system-based camera and LiDAR).…”

Section: Related Workmentioning

confidence: 99%

“…The Road event Awareness Dataset for Autonomous Driving (ROAD) [ 25 ] includes 22 videos (each 8 min long) with 122 K of annotated frames, and 560 K bounding boxes of sensing with 1.7 M individual labels. ROAD is a dataset that was designed to test the situational awareness capabilities of a robot car.…”

Section: Related Workmentioning

confidence: 99%

“…The annotation process follows a multi-label approach in which the road agents (vehicles, pedestrians, etc. ), their locations, as well as the actions they perform are identified [ 3 , 24 , 25 ].…”

Section: Related Workmentioning

confidence: 99%

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Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

Khemmar

Mauri

Dulompont

et al. 2022

Sensors

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A robust visual understanding of complex urban environments using passive optical sensors is an onerous and essential task for autonomous navigation. The problem is heavily characterized by the quality of the available dataset and the number of instances it includes. Regardless of the benchmark results of perception algorithms, a model would only be reliable and capable of enhanced decision making if the dataset covers the exact domain of the end-use case. For this purpose, in order to improve the level of instances in datasets used for the training and validation of Autonomous Vehicles (AV), Advanced Driver Assistance Systems (ADAS), and autonomous driving, and to reduce the void due to the no-existence of any datasets in the context of railway smart mobility, we introduce our multimodal hybrid dataset called ESRORAD. ESRORAD is comprised of 34 videos, 2.7 k virtual images, and 100 k real images for both road and railway scenes collected in two Normandy towns, Rouen and Le Havre. All the images are annotated with 3D bounding boxes showing at least three different classes of persons, cars, and bicycles. Crucially, our dataset is the first of its kind with uncompromised efforts on being the best in terms of large volume, abundance in annotation, and diversity in scenes. Our escorting study provides an in-depth analysis of the dataset’s characteristics as well as a performance evaluation with various state-of-the-art models trained under other popular datasets, namely, KITTI and NUScenes. Some examples of image annotations and the prediction results of our 3D object detection lightweight algorithms are available in ESRORAD dataset. Finally, the dataset is available online. This repository consists of 52 datasets with their respective annotations performed.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

Khemmar

Mauri

Dulompont

et al. 2022

Sensors

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“…Domain-specific, large-scale, and diverse datasets can fuel further advances in supervised learning. In the fast-growing field of autonomous driving, the datasets BDD-100K [26], NuScenes [27], KAIST multi-spectral driving dataset [28], KITTI [29], ROAD [54], and A2D2 [55] have proven to be of great value for computer vision tasks like object classification, object detection, and scene segmentation.…”

Section: Driving Datasetsmentioning

confidence: 99%

DeepRide: Dashcam Video Description Dataset for Autonomous Vehicle Location-Aware Trip Description

Rafiq

et al. 2022

IEEE Access

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Video description is one of the most challenging task in the combined domain of computer vision and natural language processing. 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. With the aim to explore dashcam video description generation for autonomous driving, this study presents DeepRide: a large-scale dashcam driving video description dataset for locationaware dense video description generation. The human-described dataset comprises visual scenes and actions with diverse weather, people, objects, and geographical paradigms. It bridges the autonomous driving domain with video description by textual description generation of the visual information as seen by a dashcam. We describe 16,000 videos (40 seconds each) in English employing 2,700 man-hours by two highly qualified teams with domain knowledge. The descriptions consist of eight to ten sentences covering each dashcam video's global features and event features in 60 to 90 words. The dataset consists of more than 130K sentences, totaling approximately one million words. We evaluate the dataset by employing location aware vision-language recurrent transformer framework to elaborate on the efficacy and significance of the visio-linguistics research for autonomous vehicles. We provided base line results to evaluate the dataset by employing three existing state-of-the-art recurrent models. The memory augmented transformer performed superior due to its highly summarized memory state for visual information and the sentence history while generating the trip description. Our proposed dataset opens a new dimension of diverse and exciting applications, such as self-driving vehicle reporting, driver and vehicle safety, inter-vehicle road intelligence sharing, and travel occurrence reports. INDEX TERMS dashcam video description, video captioning, autonomous trip descriptionComprehending the localized events of a video appropriately and then transforming the attained visual understand-

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“…Perception systems for AV/IV can be understood as a process that interprets the data provided by the sensors in order to understand the surrounding environment, thus contributing to safer decision-making. An important item in perception systems is the object classification part, which is currently dominated by deep network (DN) architectures [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Reducing Overconfidence Predictions in Autonomous Driving Perception

Melotti,

Premebida,

Bird

et al. 2022

IEEE Access

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Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object recognition is explored by using multisensory and multimodality approaches, with the intention of reducing the false positive rate (FPR). The reduction of the FPR becomes increasingly important in perception systems since the misclassification of an object can, depending on the circumstances, potentially cause accidents. In particular, this work presents a strategy through Bayesian inference to reduce the FPR considering the likelihood function as a cumulative distribution function from Gaussian kernel density estimations, and the prior probabilities as cumulative functions of normalized histograms. The validation of the proposed methodology is performed on the KITTI dataset using deep networks (DenseNet, NasNet, and EfficientNet), and recent 3D point cloud networks (PointNet, and PointNet++), by considering three object-categories (cars, cyclists, pedestrians) and the RGB and LiDAR sensor modalities.

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ROAD: The Road Event Awareness Dataset for Autonomous Driving

Cited by 40 publications

References 87 publications

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

Road and Railway Smart Mobility: A High-Definition Ground Truth Hybrid Dataset

DeepRide: Dashcam Video Description Dataset for Autonomous Vehicle Location-Aware Trip Description

Reducing Overconfidence Predictions in Autonomous Driving Perception

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