Development of pedestrian behavior model taking account of intention

Tamura, Yusuke; Le, Phuoc Dai; Hitomi, Kentarou; Chandrasiri, Naiwala P.; Bando, Takashi; Yamashita, Atsushi; Asama, Hajime

doi:10.1109/iros.2012.6385599

Cited by 38 publications

(19 citation statements)

References 10 publications

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“…For instance, social force models (Antonini et al 2006;Helbing and Molnár 1995;Huang et al 2017) expect agents to avoid collisions with other agents. Tamura et al (2012) extended social force towards group behavior by introducing sub-goals such as "following a person". The related Linear Trajectory Avoidance model (Pellegrini et al 2009) for short-term path prediction uses the expected point of closest approach to foreshadow and avoid possible collisions.…”

Section: Static Environment Cuesmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this probabilistic model cannot anticipate when a change in dynamic mode will occur. We propose to extract various types of cues with computer vision to provide context on the target's behavior, and incorporate these in a Dynamic Bayesian Network (DBN). The DBN extends the SLDS by conditioning the mode transition probabilities on additional context states. We describe efficient online inference in this DBN for probabilistic path prediction, accounting for uncertainty in both measurements and target behavior. Our approach is illustrated on two scenarios in the Intelligent Vehicles domain concerning pedestrians and cyclists, so-called Vulnerable Road Users (VRUs). Here, context cues include the static environment of the VRU, its dynamic environment, and its observed actions. Experiments using stereo vision data from a moving vehicle demonstrate that the proposed approach results in more accurate path prediction than SLDS at the relevant short time horizon (1 s). It slightly outperforms a computationally more demanding state-of-the-art method.

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Section: Static Environment Cuesmentioning

confidence: 99%

Context-Based Path Prediction for Targets with Switching Dynamics

et al. 2018

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“…Garcia et al [57] investigate the situation of having several robots guiding several persons, using a Social Force Model (SFM) as a means to guide a group of humans in a natural way. This model is also used by Tamura et al [94] changing the model to allow for different forces when an opportunity to follow another human exists. Social force models represent moving agents like robots or humans as masses under virtual gravitational forces.…”

Section: Natural Motionmentioning

confidence: 99%

“…Another special challenge for robots is to move in densely crowded areas. Müller et al [62] and Tamura et al [94] explore the strategy of making a robot exhibit human-like motion behavior in highly populated environments, in the sense that the robot moves along with the people who are moving in the same direction leading towards the goal of the robot.…”

Section: Natural Motionmentioning

confidence: 99%

Human-aware robot navigation: A survey

Kruse

Pandey

Alami

et al. 2013

Robotics and Autonomous Systems

609

374

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Navigation is a basic skill for autonomous robots. In the last years human-robot interaction has become an important research field that spans all of the robot capabilities including perception, reasoning, learning, manipulation and navigation. For navigation, the presence of humans requires novel approaches that take into account the constraints of human comfort as well as social rules. Besides these constraints, putting robots among humans opens new interaction possibilities for robots, also for navigation tasks, such as robot guides. This paper provides a survey of existing approaches to human-aware navigation and offers a general classification scheme for the presented methods.

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“…where does observed agent want to go), account for preferences to move around certain regions of a static scene [19], and avoid collision with other agents, as is done in social force models [2,16]. [32] enhanced social force towards group behavior by introducing sub-goals such as "following a person". The related Linear Trajectory Avoidance model [28] for short-term path prediction uses the expected point of closest approach to foreshadow and avoid possible collisions.…”

Section: Previous Workmentioning

confidence: 99%

Context-Based Pedestrian Path Prediction

Kooij

Schneider

Flohr

et al. 2014

Computer Vision – ECCV 2014

203

185

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Abstract. We present a novel Dynamic Bayesian Network for pedestrian path prediction in the intelligent vehicle domain. The model incorporates the pedestrian situational awareness, situation criticality and spatial layout of the environment as latent states on top of a Switching Linear Dynamical System (SLDS) to anticipate changes in the pedestrian dynamics. Using computer vision, situational awareness is assessed by the pedestrian head orientation, situation criticality by the distance between vehicle and pedestrian at the expected point of closest approach, and spatial layout by the distance of the pedestrian to the curbside. Our particular scenario is that of a crossing pedestrian, who might stop or continue walking at the curb. In experiments using stereo vision data obtained from a vehicle, we demonstrate that the proposed approach results in more accurate path prediction than only SLDS, at the relevant short time horizon (1 s), and slightly outperforms a computationally more demanding state-of-the-art method.

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Development of pedestrian behavior model taking account of intention

Cited by 38 publications

References 10 publications

Context-Based Path Prediction for Targets with Switching Dynamics

Context-Based Path Prediction for Targets with Switching Dynamics

Human-aware robot navigation: A survey

Context-Based Pedestrian Path Prediction

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