Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories

Kress, Viktor; Jeske, Fabian; Zernetsch, Stefan; Doll, Konrad; Sick, Bernhard

doi:10.1109/tiv.2022.3149624

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Among the agent-specific input features, the 2D position of each agent, viewed from a bird's eye view (BEV) perspective, is the most prevalent feature employed by almost all trajectory prediction methods [16,20,27,30,37,42,54,61,67,69,72,84,91,96,[100][101][102][103][104][105]. This method uses the 2D position with global or local scene coordinates depending on its application.…”

Section: Possible Input Featuresmentioning

confidence: 99%

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A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz,

Flohr

2023

Robotics

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Predicting the trajectory of other road users, especially vulnerable road users (VRUs), is an important aspect of safety and planning efficiency for autonomous vehicles. With recent advances in Deep-Learning-based approaches in this field, physics- and classical Machine-Learning-based methods cannot exhibit competitive results compared to the former. Hence, this paper provides an extensive review of recent Deep-Learning-based methods in trajectory prediction for VRUs and autonomous driving in general. We review the state and context representations and architectural insights of selected methods, divided into categories according to their primary prediction scheme. Additionally, we summarize reported results on popular datasets for all methods presented in this review. The results show that conditional variational autoencoders achieve the best overall results on both pedestrian and autonomous driving datasets. Finally, we outline possible future research directions for the field of trajectory prediction in autonomous driving.

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Section: Possible Input Featuresmentioning

confidence: 99%

“…Su et al [45] employed a pose estimator to extract the positions of 17 joints as heatmaps for further use in the model [109]. Kress et al [105] followed a different approach and provided the explicit positions of 13 joints of a VRU as input for their method.…”

Section: Possible Input Featuresmentioning

confidence: 99%

A Review of Trajectory Prediction Methods for the Vulnerable Road User

Schuetz,

Flohr

2023

Robotics

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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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Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction

de Almeida,

Mozos

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories

Cited by 3 publications

References 22 publications

A Review of Trajectory Prediction Methods for the Vulnerable Road User

A Review of Trajectory Prediction Methods for the Vulnerable Road User

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

Likely, Light, and Accurate Context-Free Clusters-based Trajectory Prediction

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