Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation

Dendorfer, Patrick; Ošep, Aljoša; Leal-Taixé, Laura

doi:10.48550/arxiv.2010.01114

Cited by 2 publications

(3 citation statements)

References 35 publications

(100 reference statements)

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“…A graph attention module was introduced to model all pedestrian interactions within a scene. A multi-modal endto-end trajectory prediction network (Goal-GAN) [35] was proposed to utilize both goal estimation and route navigation modules. The final goal location of the pedestrian was predicted based on the observed path and environmental information to generate a feasible trajectory to reach the goal.…”

Section: Trajectory Predictionmentioning

confidence: 99%

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A federated pedestrian trajectory prediction model with data privacy protection

Ni,

Lu,

Yang

et al. 2023

Complex Intell. Syst.

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Pedestrian trajectory prediction is essential for self-driving vehicles, social robots, and intelligent monitoring applications. Diverse trajectory data is critical for high-accuracy trajectory prediction. However, the trajectory data is captured in scattered scenes, which can cause the problem of data island. Furthermore, artificial aggregation of trajectory data suffers from the risk of data leakage, ignoring the rule of privacy protection. We propose a multi-scene federated trajectory prediction (Fed-TP) method to solve the above problems. As our key contribution, a destination-oriented LSTM (Long Short-Term Memory)-based trajectory prediction (DO-TP) network is proposed in each scene to forecast future trajectories in an encoder-decoder manner. The independent training using trajectory data in each scene can prevent data leakage and achieves high privacy security. As another key contribution, a federated learning framework is introduced to break the scene limitation by conducting distributed collaborative training. The performance of different federated learning methods is compared on public datasets, including ETH, UCY, and Stanford Drone Dataset (SDD). Compared with FedAvg and FedProx, FedAtt is more suitable for pedestrian trajectory prediction. Experimental results demonstrate that the proposed method has better data privacy security than directly training on multiple scenes and superior prediction performance than training on a single scene.

show abstract

Section: Trajectory Predictionmentioning

confidence: 99%

“…The proposed method is evaluated on three public datasets, including ETH [45], UCY [46], and Stanford Drone Dataset (SDD) [47], which are widely used for pedestrian trajectory prediction [32,33,35,36,48]. These datasets are taken from diverse scenes, including the hotel, university, zara, and different places at Stanford.…”

Section: Datasetsmentioning

confidence: 99%

A federated pedestrian trajectory prediction model with data privacy protection

Ni,

Lu,

Yang

et al. 2023

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…More recent approaches, such as systems in [ 32 , 33 ], are using goal-based prediction methods that proved to be effective for trajectory prediction. These kinds of methods are sampling multiple possible goal candidates for a person and choosing the best fit from that set of candidates.…”

Section: Related Workmentioning

confidence: 99%

Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach

Ghita

Florea

2022

Sensors

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Currently, the importance of autonomous operating devices is rising with the increasing number of applications that run on robotic platforms or self-driving cars. The context of social robotics assumes that robotic platforms operate autonomously in environments where people perform their daily activities. The ability to re-identify the same people through a sequence of images is a critical component for meaningful human-robot interactions. Considering the quick reactions required by a self-driving car for safety considerations, accurate real-time tracking and people trajectory prediction are mandatory. In this paper, we introduce a real-time people re-identification system based on a trajectory prediction method. We tackled the problem of trajectory prediction by introducing a system that combines semantic information from the environment with social influence from the other participants in the scene in order to predict the motion of each individual. We evaluated the system considering two possible case studies, social robotics and autonomous driving. In the context of social robotics, we integrated the proposed re-identification system as a module into the AMIRO framework that is designed for social robotic applications and assistive care scenarios. We performed multiple experiments in order to evaluate the performance of our proposed method, considering both the trajectory prediction component and the person re-identification system. We assessed the behaviour of our method on existing datasets and on real-time acquired data to obtain a quantitative evaluation of the system and a qualitative analysis. We report an improvement of over 5% for the MOTA metric when comparing our re-identification system with the existing module, on both evaluation scenarios, social robotics and autonomous driving.

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Goal-GAN: Multimodal Trajectory Prediction Based on Goal Position Estimation

Cited by 2 publications

References 35 publications

A federated pedestrian trajectory prediction model with data privacy protection

A federated pedestrian trajectory prediction model with data privacy protection

Real-Time People Re-Identification and Tracking for Autonomous Platforms Using a Trajectory Prediction-Based Approach

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