Behavioral dynamics of steering, obstacle avoidance, and route selection.

By Brett R. Fajen, William H. Warren Jr.. Journal of Experimental Psychology: Human Perception and Performance, 2003

507 citations recorded by the source corpus.

The authors investigated the dynamics of steering and obstacle avoidance, with the aim of predicting routes through complex scenes. Participants walked in a virtual environment toward a goal (Experiment 1) and around an obstacle (Experiment 2) whose initial angle and distance varied. Goals and obstacles behave as attractors and repellers of heading, respectively, whose strengths depend on distance. The observed behavior was modeled as a dynamical system in which angular acceleration is a function of goal and obstacle angle and distance. By linearly combining terms for goals and obstacles, one could predict whether participants adopt a route to the left or right of an obstacle to reach a go (Experiment 3). Route selection may emerge from on-line steering dynamics, making explicit path planning unnecessary.

Subfields: Automotive Engineering, Ocean Engineering, Social Psychology

View Paper: https://www.semanticscholar.org/paper/657b214778df346965d3dbbf72df400da53a8c68

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Citations in the corpus

Kernelized movement primitives (2017, unknown): “jectories so as to fulﬁll relevant task requirements. Take the obstacle avoidance as an example, the relation between human behaviors and obstacles (with varied angles and positions) can be exploited [20], which could then be combined with KMP to re-plan trajectories. 6 Conclusions We have proposed ”
Modeling Driver Behavior in a Cognitive Architecture (2006, background): “A related class of models arises in recent perception-action models of control (e.g., Fajen & Warren, 2003; Rushton et al., 1998; Salvucci & Gray, 2004; Wilkie & Wann, 2003).”
The development of decision making skill in sport : an ecological dynamics perspective (2009, background): “Moreover, agent–environment interactions give rise to emergent behavior that has a dynamics of its own, which Fajen and Warren (2003) call behavioral dynamics.”
A Two-Point Visual Control Model of Steering (2004, methodology): “This controller is a similar to the point-attractor approach incorporated into related models of control (e.g., Wilkie & Wann, 2003; Fajen & Warren, 2003) as will be discussed in detail below.”
Key Properties of Expert Movement Systems in Sport (2013, background): “Such a ‘behavioral dynamics’ approach captures the emergence of adaptive functional behaviours by an individual in a performance environment, coupled by perceptual information [24, 50, 51].”
Flying Fast and Low Among Obstacles: Methodology and Experiments (2008, methodology): “The local planning for both the 3D and 2D obstacle avoidance is related to models of human navigation among discrete obstacles (Fajen and Warren 2003).”
Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance (2009, background): “Fajen and Warren [13] found a differential equation that models human obstacle avoidance.”
The dynamics of perception and action. (2006, methodology): “The second level of analysis is a low-dimensional description of this global behavior, the behavioral dynamics (Fajen & Warren, 2003).”
Learning and generalization of motor skills by learning from demonstration (2009, background): “The expression (10) is derived from [10] and empirically matches human obstacle avoidance.”
Embodied Cognition is Not What you Think it is (2013, methodology): “The standard cognitive psychology explanation for the form of behavior is that it reflects the contents and operation of an internal algorithm (implemented as a mental representation) designed to produce that behavior on demand (e.g., Fodor, 1975, 2008).”