openDD: A Large-Scale Roundabout Drone Dataset

Breuer, Antonia; Termöhlen, Jan-Aike; Homoceanu, Silviu; Fingscheidt, Tim

doi:10.1109/itsc45102.2020.9294301

Cited by 30 publications

(15 citation statements)

References 7 publications

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“…However, we also find that a number of datasets have not been widely used, affected by their own limitations. For example, the rounD [205] and Opendd [206] can only be used for validating the behavior planning of ADS in the scenario of roundabouts; SYNTHIA [207], GTA [208] and Apollo Synthetic [209] contain synthetic images from virtual environments, which may be not realistic enough for ADS testing.…”

Section: Datasets and Toolsets For Ads Testingmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Automated Driving System Testing: Landscapes and Trends

Tang¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning and control, which brings together the latest advances in multiple domains. Despite these achievements, safety assurance of the systems is still of great significance, since the unsafe behavior of ADS can bring catastrophic consequences and unacceptable economic and social losses. Testing is an important approach to system validation for the deployment in practice; in the context of ADS, it is extremely challenging, due to the system complexity and multidisciplinarity. There has been a great deal of literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. However, most of these surveys focus on the system-level testing that is performed within software simulators, and thereby ignore the distinct features of individual modules. In this paper, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) we build a threat model that reveals the potential safety threats for each module of an ADS; (2) we survey the module-level testing techniques for ADS and highlight the technical differences affected by the properties of the modules; (3) we also survey the system-level testing techniques, but we focus on empirical studies that take a bird's-eye view on the system, the problems due to the collaborations between modules, and the gaps between ADS testing in simulators and real world; (4) we identify the challenges and opportunities in ADS testing, which facilitates the future research in this field. CCS Concepts: • Computer systems organization → Embedded systems; • Software and its engineering → Software verification and validation; • Security and privacy → Systems security; • Computing methodologies → Artificial intelligence.

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Section: Datasets and Toolsets For Ads Testingmentioning

confidence: 99%

“…The rounD [205] Naturalistic trajectories of vehicles and vulnerable road users recorded at German roundabouts videos -2020 Opendd [206] An trajectory dataset covering seven roundabouts 62h vedios -2021…”

Section: 3g Videos -2020mentioning

confidence: 99%

A Survey on Automated Driving System Testing: Landscapes and Trends

Tang¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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show abstract

“…Camera: Many of them provide camera images or sequences (cf. [1]), such as the Cityscapes dataset [32], the BDD100k dataset [33], the openDD dataset [34], or the Mapillary Vistas dataset [35], to name a few. All of those are large-scale datasets for visual perception and provide adequate labels for the related tasks such as semantic and instance segmentation and object detection.…”

Section: A Datasetsmentioning

confidence: 99%

An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Heidecker,

Breitenstein,

Rösch

et al. 2021

Preprint

Self Cite

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Systems and functions that rely on machine learning (ML) are the basis of highly automated driving. An essential task of such ML models is to reliably detect and interpret unusual, new, and potentially dangerous situations. The detection of those situations, which we refer to as corner cases, is highly relevant for successfully developing, applying, and validating automotive perception functions in future vehicles where multiple sensor modalities will be used. A complication for the development of corner case detectors is the lack of consistent definitions, terms, and corner case descriptions, especially when taking into account various automotive sensors. In this work, we provide an application-driven view of corner cases in highly automated driving. To achieve this goal, we first consider existing definitions from the general outlier, novelty, anomaly, and out-of-distribution detection to show relations and differences to corner cases. Moreover, we extend an existing camera-focused systematization of corner cases by adding RADAR (radio detection and ranging) and LiDAR (light detection and ranging) sensors. For this, we describe an exemplary toolchain for data acquisition and processing, highlighting the interfaces of the corner case detection. We also define a novel level of corner cases, the method layer corner cases, which appear due to uncertainty inherent in the methodology or the data distribution.

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“…For each participant they include their pose, velocity, acceleration, size, and also the frames where it appears. Within the considered public available datasets (INTERACTION [4], rounD [5], and openDD [6]), 9 different roundabouts were identified, from which the situation with a higher interactivity, was selected.…”

Section: A Dataset Processingmentioning

confidence: 99%

“…In this paper, a method to classify the intentions of the surrounding vehicles at unsignalized roundabouts is proposed. The framework here presented is evaluated in 9 roundabouts with data from the datasets INTERACTION [4], rounD [5], and openDD [6]. The main contributions of this article are:…”

Section: Introductionmentioning

confidence: 99%

Interaction-Aware Intention Estimation at Roundabouts

Trentin¹,

Artuñedo

Godoy

et al. 2021

IEEE Access

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Roundabouts have many benefits when compared with traditional signal-controlled intersections: improve safety, reduce delay, improve traffic flow, are less expensive and occupy less area. The behavior of traffic participants is full of uncertainties in the real world. An automated system that relies only on its perception is unable to safely enter the roundabout until a large gap occurs or the vehicle approaching has actually left the roundabout or passed the conflict area. In order to improve the driving quality, autonomous vehicles should be able to infer the correct intention at roundabout as early as possible. In this work, a method to classify the intentions of the surrounding vehicles at unsignalized roundabouts is proposed. For each vehicle at the scene, a Dynamic Bayesian Network is instantiated and the intentions are inferred using a particle filter.

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openDD: A Large-Scale Roundabout Drone Dataset

Cited by 30 publications

References 7 publications

A Survey on Automated Driving System Testing: Landscapes and Trends

A Survey on Automated Driving System Testing: Landscapes and Trends

An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Driving

Interaction-Aware Intention Estimation at Roundabouts

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