The ApolloScape Open Dataset for Autonomous Driving and Its Application

Huang, Xinyu; Wang, Peng; Cheng, Xinjing; Zhou, Dingfu; Geng, Qichuan; Yang, Ruigang

doi:10.1109/tpami.2019.2926463

Cited by 348 publications

(150 citation statements)

References 58 publications

Supporting

Mentioning

148

Contrasting

Unclassified

Order By: Relevance

“…Each scenario contains the 2D, birdseye-view centroid of each tracked object. ApolloScape dataset [214] was recorded in urban areas in China using various sensors. The dataset contains different road road users (vehicles, pedestrians, bicycles).…”

Section: A Pedestrian Datasetsmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static and kinematic obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from lowlevel image detection to high-level psychological models. This selfcontained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians' likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control.

show abstract

Section: A Pedestrian Datasetsmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Besides using learned features that are more robust to changes in viewing conditions, long-term localization approaches also use semantic image segmentation (Budvytis et al 2019;Garg et al 2019;Larsson et al 2019;Stenborg et al 2018;Schönberger et al 2018;Seymour et al 2019;Shi et al 2019;Taira et al 2019;Toft et al 2017Toft et al , 2018Wang et al 2019;Yu et al 2018). These methods are based on the observation that the semantic meaning of scene elements, in contrast to their appearance, is invariant to changes.…”

Section: Semantic Visual Localizationmentioning

confidence: 99%

Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis

2020

View full text Add to dashboard Cite

Visual Localization is one of the key enabling technologies for autonomous driving and augmented reality. High quality datasets with accurate 6 Degree-of-Freedom (DoF) reference poses are the foundation for benchmarking and improving existing methods. Traditionally, reference poses have been obtained via Structure-from-Motion (SfM). However, SfM itself relies on local features which are prone to fail when images were taken under different conditions, e.g., day/night changes. At the same time, manually annotating feature correspondences is not scalable and potentially inaccurate. In this work, we propose a semi-automated approach to generate reference poses based on feature matching between renderings of a 3D model and real images via learned features. Given an initial pose estimate, our approach iteratively refines the pose based on feature matches against a rendering of the model from the current pose estimate. We significantly improve the nighttime reference poses of the popular Aachen Day–Night dataset, showing that state-of-the-art visual localization methods perform better (up to 47%) than predicted by the original reference poses. We extend the dataset with new nighttime test images, provide uncertainty estimates for our new reference poses, and introduce a new evaluation criterion. We will make our reference poses and our framework publicly available upon publication.

show abstract

“…Subsequently, another popular dataset is released named ApolloScape [21]. Compared with Kitti and Cityscape, it contains an extensive amount of data and has many properties.…”

Section: Datasetmentioning

confidence: 99%

“…Nonetheless, Kitti dataset is broadly used in this field of research, the variety of its recording environmental conditions is reduced: it is gathered only under daytime and sunlit days, similar to CamVid, CityScapes and H3D datasets. In order to enrich light recording conditions [13,14,20,21,23] collected data considering both daytime and night time all day long. Concerning the diversity of weather conditions, only BDD100k and nuScenes covers rain and snow situations.…”

Section: Datasetmentioning

confidence: 99%

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

et al. 2020

View full text Add to dashboard Cite

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

show abstract

The ApolloScape Open Dataset for Autonomous Driving and Its Application

Cited by 348 publications

References 58 publications

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Contact Info

Product

Resources

About