3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

Sun, Li; Yan, Zhi; Mellado, Sergi Molina; Hanheide, Marc; Duckett, Tom

doi:10.48550/arxiv.1710.00126

Cited by 3 publications

(3 citation statements)

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“…RNNs use a fully connected two-layer neural network within which the hidden layer implements a feedback loop, allowing sequential data to be modelled more efficiently. Some work uses the LSTM structure to learn pedestrian activity patterns and the environment within a scenario over a long-term period [187][188][189], the human body pose [190], and the influence of human [168].…”

Section: Trajectory and Trackingmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Section: Trajectory and Trackingmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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“…Forecasting trajectories from images, however, is a complex problem and, probably for this reason, it has only recently emerged as a popular computer vision research topic. In particular, the modern re-visitation of Long Short Term Memory (LSTM) architectures [41], has enabled a leap forward in performance [31], [34], [72], [73], [78]. On one side, LSTM has allowed a seamless encoding of the social interplay among pedestrians [3], [31].…”

Section: Introductionmentioning

confidence: 99%

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

Hasan

Setti

Tsesmelis

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.

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“…Anticipating the trajectories that could occur in the future is important for several reasons: in computer vision, path forecasting helps the dynamics modeling for target tracking [40,47,48,59] and behavior understanding [3,30,33,35,47]; in robotics, autonomous systems should plan routes that will avoid collisions and be respectful of the human proxemics [13,21,31,36,53,62]. Recently, path forecasting has benefited from the introduction of Long Short Term Memory (LSTM) architectures [3,22,26,50,51,55].…”

Section: Introductionmentioning

confidence: 99%

MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses

Hasan

Setti

Tsesmelis

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

126

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Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. At the same time, MX-LSTM predicts the future head poses, increasing the standard capabilities of the long-term trajectory forecasting approaches. With standard head pose estimators and an attentional-based social pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic margin when the pedestrians slow down, a case where most of the forecasting approaches struggle to provide an accurate solution.

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3DOF Pedestrian Trajectory Prediction Learned from Long-Term Autonomous Mobile Robot Deployment Data

Cited by 3 publications

References 0 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses

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