The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Krajewski, Robert J.; Bock, Julian; Kloeker, Laurent; Eckstein, Lutz

doi:10.48550/arxiv.1810.05642

Cited by 4 publications

(4 citation statements)

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“…This issue will most likely be overcome by using additional training data with extensive coverage of road topologies. In recent years, many suitable datasets have been publicly released [31], [32], [33], [34], [35] and training ITRA on them is one of our directions for future work. We note, however, that those datasets come in substantially different formats, particularly regarding the map specification, which requires a significant amount of work to incorporate them into our differentiable simulator.…”

Section: Discussionmentioning

confidence: 99%

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation

Ścibior¹,

Lioutas²,

Reda³

et al. 2021

Preprint

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We develop a deep generative model built on a fully differentiable simulator for multi-agent trajectory prediction. Agents are modeled with conditional recurrent variational neural networks (CVRNNs), which take as input an ego-centric birdview image representing the current state of the world and output an action, consisting of steering and acceleration, which is used to derive the subsequent agent state using a kinematic bicycle model. The full simulation state is then differentiably rendered for each agent, initiating the next time step. We achieve state-of-the-art results on the INTERACTION dataset, using standard neural architectures and a standard variational training objective, producing realistic multi-modal predictions without any ad-hoc diversity-inducing losses. We conduct ablation studies to examine individual components of the simulator, finding that both the kinematic bicycle model and the continuous feedback from the birdview image are crucial for achieving this level of performance. We name our model ITRA, for "Imagining the Road Ahead".

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Section: Discussionmentioning

confidence: 99%

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation

Ścibior¹,

Lioutas²,

Reda³

et al. 2021

Preprint

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show abstract

“…As mentioned in Section 2, high-resolution vehicle trajectory data can be widely applied to risk assessment of multi-vehicle interaction [59,60]. State inference-based evaluation methods usually directly depict and deduce current and future risk states using the historical trajectories of vehicles instead of their predicted future trajectories.…”

Section: Evaluation Methods Based On State Inferencementioning

confidence: 99%

Review of Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios

Xiong,

Zhang,

Chen

2023

WEVJ

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With the rapid breakthroughs in artificial intelligence technology and intelligent manufacturing technology, automotive intelligence has become a research hotspot, and much progress has been made. However, a skeptical attitude is still held towards intelligent vehicles, especially when driving in a complex multi-vehicle interaction environment. The interaction among multi-vehicles generally involves more uncertainties in vehicle motion and entails higher driving risk, and thus deserves more research concerns and efforts. Targeting the safety assessment issue of complex multi-vehicle interaction scenarios, this article summarizes the existing literature on the relevant data collection methodologies, vehicle interaction mechanisms, and driving risk evaluation methods for intelligent vehicles. The limitations of the existing assessment methods and the prospects for their future development are analyzed. The results of this article can provide a reference for intelligent vehicles in terms of timely and accurate driving risk assessment in real-world multi-vehicle scenarios and help improve the safe driving technologies of intelligent vehicles.

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“…This dataset is part of a set of vehicle trajectories data provided by the Institute for Automotive Engineering (ika) in RWTH Aachen University. One may cite the highD dataset (about highways) [31] and the inD dataset (about intersections) [3], such as other ones produced by ika. These datasets are particularly useful for studying the behavior of road users in some specific situations.…”

Section: Real Data Analysis: the Round Datasetmentioning

confidence: 99%

Clustering multivariate functional data using unsupervised binary trees

Golovkine,

Klutchnikoff,

Patilea

2020

Preprint

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We propose a model-based clustering algorithm for a general class of functional data for which the components could be curves or images. The random functional data realizations could be measured with error at discrete, and possibly random, points in the definition domain. The idea is to build a set of binary trees by recursive splitting of the observations. The number of groups are determined in a data-driven way. The new algorithm provides easily interpretable results and fast predictions for online data sets. Results on simulated datasets reveal good performance in various complex settings. The methodology is applied to the analysis of vehicle trajectories on a German roundabout.

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The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems

Cited by 4 publications

References 0 publications

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation

Review of Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios

Clustering multivariate functional data using unsupervised binary trees

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