Real Time Trajectory Prediction Using Deep Conditional Generative Models

Gómez-González, Sebastián; Prokudin, Sergey; Schölkopf, Bernhard; Peters, Jan

doi:10.1109/lra.2020.2966390

Cited by 31 publications

(16 citation statements)

References 13 publications

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“…A new trend in representation of the acting agents for neural network models, were graph models [102][103][104][105][106][107][108][109]. Describing the connections between objects in a graph structure in opposition to an occupancy grid, is a huge advantage if there are only sparse connections between the objects.…”

Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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Towards the aim of mastering level 5, a fully automated vehicle needs to be equipped with sensors for a 360∘ surround perception of the environment. In addition to this, it is required to anticipate plausible evolutions of the traffic scene such that it is possible to act in time, not just to react in case of emergencies. This way, a safe and smooth driving experience can be guaranteed. The complex spatio-temporal dependencies and high dynamics are some of the biggest challenges for scene prediction. The subtile indications of other drivers’ intentions, which are often intuitively clear to the human driver, require data-driven models such as deep learning techniques. When dealing with uncertainties and making decisions based on noisy or sparse data, deep learning models also show a very robust performance. In this survey, a detailed overview of scene prediction models is presented with a historical approach. A quantitative comparison of the model results reveals the dominance of deep learning methods in current state-of-the-art research in this area, leading to a competition on the cm scale. Moreover, it also shows the problem of inter-model comparison, as many publications do not use standardized test sets. However, it is questionable if such improvements on the cm scale are actually necessary. More effort should be spent in trying to understand varying model performances, identifying if the difference is in the datasets (many simple situations versus many corner cases) or actually an issue of the model itself.

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Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

View full text Add to dashboard Cite

show abstract

“…Considering the multi-modal distribution of the agent's future trajectory, the deep generation model has become the most advanced trajectory prediction method currently. 19,20 For most trajectory generation models, deep recursive backbone architectures are used with latent variable models, such as CVAE, 20 which explicitly encode the multi-modal distribution of the trajectory [21][22][23][24] or GAN, 19 implicitly to learn the distribution. [25][26][27][28] Among them, Trajectron 23 and Social-BiGAT 27 are considered as the best performance models based on CVAE and GAN on the evaluation benchmarks respectively.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic trajectory prediction of heterogeneous traffic agents based on layered spatio-temporal graph

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In order to safely and comfortably navigate in the complex urban traffic, it is necessary to make multi-modal predictions of autonomous vehicles for the next trajectory of various traffic participants, with the continuous movement trend and inertia of the surrounding traffic agents taken into account. At present, most trajectory prediction methods focus on prediction on future behavior of traffic agents but with limited, consideration of the response of traffic agents to the future behavior of the ego-agent. Moreover, it can only predict the trajectory of single-type agents, which make it impossible to learn interaction in a complex environment between traffic agents. In this paper, we proposed a graph-based heterogeneous traffic agents trajectory prediction model LSTGHP, which consists of the following three parts: (1) layered spatio-temporal graph module; (2) ego-agent motion module; (3) trajectory prediction module, which can realize multi-modal prediction of future trajectories of traffic agents with different semantic categories in the scene. To evaluate its performance, we collected trajectory datasets of heterogeneous traffic agents in a time-varying, highly dynamic urban intersection environment, where vehicles, bicycles, and pedestrians interacted with each other in the scene. It can be drawn from experimental results that our model can improve its prediction accuracy while interacting at a close range. Compared with the previous prediction methods, the model has less prediction error in the trajectory prediction of heterogeneous traffic agents.

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“…Naturally, sports analytics is not the only domain where movement models are prominently deployed. Besides the already mentioned areas, other applications include vehicle trajectory analysis (Besse et al 2018), pedestrian trajectory estimation from videos (Zhong et al 2020), and real-time robot trajectory prediction (Gomez-Gonzalez et al 2020).…”

Section: Related Workmentioning

confidence: 99%

Contextual movement models based on normalizing flows

et al. 2021

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Movement models predict positions of players (or objects in general) over time and are thus key to analyzing spatiotemporal data as it is often used in sports analytics. Existing movement models are either designed from physical principles or are entirely data-driven. However, the former suffers from oversimplifications to achieve feasible and interpretable models, while the latter relies on computationally costly, from a current point of view, nonparametric density estimations and require maintaining multiple estimators, each responsible for different types of movements (e.g., such as different velocities). In this paper, we propose a unified contextual probabilistic movement model based on normalizing flows. Our approach learns the desired densities by directly optimizing the likelihood and maintains only a single contextual model that can be conditioned on auxiliary variables. Training is simultaneously performed on all observed types of movements, resulting in an effective and efficient movement model. We empirically evaluate our approach on spatiotemporal data from professional soccer. Our findings show that our approach outperforms the state of the art while being orders of magnitude more efficient with respect to computation time and memory requirements.

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Real Time Trajectory Prediction Using Deep Conditional Generative Models

Cited by 31 publications

References 13 publications

A Review on Scene Prediction for Automated Driving

A Review on Scene Prediction for Automated Driving

Probabilistic trajectory prediction of heterogeneous traffic agents based on layered spatio-temporal graph

Contextual movement models based on normalizing flows

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