Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately.

Precise assessment of motor deficits after traumatic spinal cord injury (SCI) in rodents is crucial for understanding the mechanisms of functional recovery and testing therapeutic approaches. Here we analyzed the applicability to a rat SCI model of an objective approach, the single-frame motion analysis, created and used for functional analysis in mice. Adult female Wistar rats were subjected to graded compression of the spinal cord. Recovery of locomotion was analyzed using video recordings of beam walking and inclined ladder climbing. Three out of four parameters used in mice appeared suitable: the foot-stepping angle (FSA) and the rump-height index (RHI), measured during beam walking, and for estimating paw placement and body weight support, respectively, and the number of correct ladder steps (CLS), assessing skilled limb movements. These parameters, similar to the Basso, Beattie, and Bresnahan (BBB) locomotor rating scores, correlated with lesion volume and showed significant differences between moderately and severely injured rats at 1-9 weeks after SCI. The beam parameters, but not CLS, correlated well with the BBB scores within ranges of poor and good locomotor abilities. FSA co-varied with RHI only in the severely impaired rats, while RHI and CLS were barely correlated. Our findings suggest that the numerical parameters estimate, as intended by design, predominantly different aspects of locomotion. The use of these objective measures combined with BBB rating provides a time- and cost-efficient opportunity for versatile and reliable functional evaluations in both severely and moderately impaired rats, combining clinical assessment with precise numerical measures.

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