Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks

Zernetsch, Stefan; Reichert, Hannes; Kress, Viktor; Doll, Konrad; Sick, Bernhard

doi:10.1109/ivs.2019.8814258

Cited by 18 publications

(18 citation statements)

References 10 publications

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“…By performing basic movement detection to generate weights for different movement types and training movement specific forecast models, we are capable of forecasting multimodal distributions. Furthermore we extend the evaluation approach from [19] for arbitrary distributions and show that our approach outperforms [19] in terms of reliability.…”

Section: B Related Workmentioning

confidence: 96%

“…To evaluate our results, we developed two methods which are capable of rating the reliability and sharpness of arbitrary distributions. By using the method from [19] as baseline, we show that our algorithm is able to produce more reliable and sharper forecasts with comparable positional accuracy. Both methods are evaluated using a dataset, which we created at an urban intersection in real world traffic scenarios.…”

Section: Main Contributions and Outline Of This Papermentioning

confidence: 96%

“…While all the methods mentioned above create probabilistic forecasts, the reliability of the produced outputs is not verified. In [19], a similar approach to forecast cyclist trajectories is used and a method to evaluate the reliability of the forecasted distributions is proposed. However, the method is limited to unimodal Gaussian distributions.…”

Section: B Related Workmentioning

confidence: 99%

“…The article also shows that modeling future trajectories with unimodal Gaussian distributions leads to underconfident estimations. In this work, we extend the approach from [19]. By performing basic movement detection to generate weights for different movement types and training movement specific forecast models, we are capable of forecasting multimodal distributions.…”

Section: B Related Workmentioning

confidence: 99%

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Cyclist Intention Detection: A Probabilistic Approach

Zernetsch¹,

Reichert²,

Kress³

et al. 2021

Preprint

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This article presents a holistic approach for probabilistic cyclist intention detection. A basic movement detection based on motion history images (MHI) and a residual convolutional neural network (ResNet) are used to estimate probabilities for the current cyclist motion state. These probabilities are used as weights in a probabilistic ensemble trajectory forecast. The ensemble consists of specialized models, which produce individual forecasts in the form of Gaussian distributions under the assumption of a certain motion state of the cyclist (e.g. cyclist is starting or turning left). By weighting the specialized models, we create forecasts in the from of Gaussian mixtures that define regions within which the cyclists will reside with a certain probability. To evaluate our method, we rate the reliability, sharpness, and positional accuracy of our forecasted distributions. We compare our method to a single model approach which produces forecasts in the form of Gaussian distributions and show that our method is able to produce more reliable and sharper outputs while retaining comparable positional accuracy. Both methods are evaluated using a dataset created at a public traffic intersection. Our code and the dataset are made publicly available.

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Section: B Related Workmentioning

confidence: 96%

Section: Main Contributions and Outline Of This Papermentioning

confidence: 96%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Cyclist Intention Detection: A Probabilistic Approach

Zernetsch¹,

Reichert²,

Kress³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…b) Probability distribution: To consider that other traffic participants have infinitely many future behaviors, we can compute a probability distribution, e. g., of kinematic variables using dynamic Bayesian networks [51]- [53]. Furthermore, neural networks have been proposed to predict most likely behaviors of vehicles on highways [54], [55], of pedestrians [56], and of cyclists [57]. For pedestrians, also linear quadratic regulator-based models are used [58].…”

Section: A Related Workmentioning

confidence: 99%

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Koschi

Althoff

2021

IEEE Trans. Intell. Veh.

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Provably safe motion planning for automated road vehicles must ensure that planned motions do not result in a collision with other traffic participants. This is a major challenge in autonomous driving, since the future behavior of other traffic participants is not known and since traffic participants are often hidden due to occlusions. In this work, we propose a formal setbased prediction that contains all acceptable future behaviors of both detected and potentially hidden traffic participants. Based on formalized traffic rules and nondeterministic motion models, we perform reachability analysis to predict the set of possible occupancies and velocities of vehicles, pedestrians, and cyclists. Real-world experiments with a test vehicle in various traffic situations demonstrate the applicability and real-time capability of our over-approximative prediction for both online verification and fail-safe trajectory planning. Even in congested, complex traffic scenarios, our forecasting approach enables self-driving vehicles to never cause accidents.

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Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving

Zernetsch,

Kress,

Bieshaar

et al. 2024

Cooperatively Interacting Vehicles

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The project Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving (DeCoInt$$^2$$ 2 ) focuses on detecting the intentions of vulnerable road users (VRUs) in automated driving using cooperative technologies. Especially in urban areas, VRUs, e.g., pedestrians and cyclists, will continue to play an essential role in mixed traffic. For an accident-free and highly efficient traffic flow with automated vehicles, it is vital to perceive VRUs and their intentions and analyze them similarly to humans when driving and forecasting VRU trajectories. Doing this reliably and robustly with a multimodal sensor system (e.g., cameras, LiDARs, accelerometers, and gyroscopes in mobile devices) in real-time is a big challenge. We follow a holistic, cooperative approach to recognize humans’ movements and forecast their trajectories. Heterogeneous open sets of agents, i.e., collaboratively interacting vehicles, infrastructure, and VRUs equipped with mobile devices, exchange information to determine individual models of their surrounding environment, allowing an accurate and reliable forecast of VRU basic movements and trajectories. The collective intelligence of cooperating agents resolves occlusions, implausibilities, and inconsistencies. We developed new methods by considering and combining novel signal processing and modeling techniques with machine learning-based pattern recognition approaches. The cooperation between agents happens on several levels: the VRU perception level, the level of recognized trajectories, or the level of already detected intentions.

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Trajectory Forecasts with Uncertainties of Vulnerable Road Users by Means of Neural Networks

Cited by 18 publications

References 10 publications

Cyclist Intention Detection: A Probabilistic Approach

Cyclist Intention Detection: A Probabilistic Approach

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

Detecting Intentions of Vulnerable Road Users Based on Collective Intelligence as a Basis for Automated Driving

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