Set-Based Prediction of Pedestrians in Urban Environments Considering Formalized Traffic Rules

Abstract

Set-based predictions can ensure the safety of planned motions, since they provide a bounded region which includes all possible future states of nondeterministic models of other traffic participants. However, while autonomous vehicles are tested in urban environments, a set-based prediction tailored to pedestrians does not exist yet. This paper addresses this problem and presents an approach for set-based predictions of pedestrians using reachability analysis. We obtain tight overapproximations of pedestrians' reachable occupancy by incorporating the dynamics of pedestrians, contextual information, and traffic rules. In addition, since pedestrians often disregard traffic rules, our constraints automatically adapt so that such behaviors are included in the prediction. Using datasets of recorded pedestrians, we validate our proposed method and demonstrate its use for evasive maneuver planning of automated vehicles.

Set-Based Prediction of Traffic Participants Considering Occlusions and Traffic Rules

“…This work significantly extends our previous work on setbased prediction [14], [67], [69], [70] and other previous works, especially [16], by considering 1) all safety-relevant occluded vehicles, pedestrians, and static obstacles, 2) priorities of traffic participants at intersections, 3) safe distances to the ego vehicle, 4) limited turning radii of vehicles, and 5) by validating the prediction in real-world experiments.…”

“…The work of [67] is extended in [16] by considering occlusions. Set-based prediction is also able to consider interaction between traffic participants [69] and formalized traffic rules [14], [70]. The predicted occupancy sets can also be weighted by probabilities [71], [72] d) Occlusion: The risk from occlusions is tackled either by shrinking the field of view over the prediction horizon [73]- [76] or by introducing and predicting individual, potentially present obstacles (aka phantom or virtual objects) [1], [16], [77]- [85].…”

“…Since pedestrians often do not observe the priority of vehicular traffic, e. g., by jaywalking, O ped prio (t; t 0 ) can be extended to a more sophisticated prediction of pedestrians stepping on the road and potentially crossing it as described in [70,.…”

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

“…The robot can track and predict the future trajectory of the person by maximizing its reward at future steps while avoiding entering into the human's personal space. Koschi et al [116] proposed a set-based method to predict all possible behaviours of pedestrians using reachability analysis [5] for pedestrian occupancy. Pedestrians are described as point mass with a certain maximum velocity and maximum acceleration.…”

Pedestrian Collision Avoidance System for Scenarios with Occlusions

“…The representation of the pedestrian needs 27 longitudinal positions, eleven lateral positions, five velocity levels and seven possible orientations. By multiplying all possible combinations of ego and pedestrian states, the total number of states amounts to 1.5 × 10 6 .…”