Abstract
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.
Highlights
Anticipating how nearby objects will behave is a key challenge in various application domains, such as intelligent vehicles, social robotics, and surveillance
To improve path prediction of objects with switching dynamics, we propose to exploit context cues that can be extracted from sensor data
We focus on applications in the Intelligent Vehicle (IV) domain
Summary
Anticipating how nearby objects will behave is a key challenge in various application domains, such as intelligent vehicles, social robotics, and surveillance. These domains concern systems that navigate trough crowded environments, Communicated by Larry Davis. To improve path prediction of objects with switching dynamics, we propose to exploit context cues that can be extracted from sensor data. Vision can provide measurements for a diverse set of relevant cues. Incorporating more observations in the prediction process increases sensitivity to measurement uncertainty. Uncertainty is an inherent property of any prediction on future events. We leverage existing probabilistic filters for switching dynamics, which are common for tracking maneuvering targets
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