Pedestrian Modeling

Developing hybrid-automata models of long-term urban pedestrian behavior

For automated vehicles (AVs) to navigate safely, they must be able to anticipate and predict the behavior of pedestrians. This is particularly critical in urban driving environments where risks of collisions are high. However, a major challenge is that pedestrian behavior is inherently multimodal in nature, i.e., pedestrians can plausibly take multiple paths. This is because, in large part, pedestrian behaviors are driven by unique intentions and decisions made by each pedestrian walking along a particular sidewalk or crosswalk. As described in this paper, we developed a hybrid automaton model of multimodal pedestrian behavior called Multimodal Hybrid Pedestrian (MHP). We account for multimodal pedestrian behavior by identifying pedestrian decision-making points and developing decision-making models to predict pedestrian behaviors in a probabilistic hybrid automaton framework.

On the left, is a typical pedestrian crossing scenario at an unsignalized crosswalk. Right, shows the hybrid automaton model of a pedestrian.

The resulting automaton model is more likely to predict the ground truth trajectory compared to two baseline models - a baseline hybrid automaton model and a constant velocity model. The MHP model is applicable to a wide variety of urban scenarios - midblocks, intersections, one-way, and two-way streets, etc., and the probabilistic predictions from the model can be utilized for AV motion planning.

e 1p, e 2p, and e 3p are prediction envelopes at prediction time step t p. e 1p is the prediction envelope of the constant velocity model and e 2p and e 3p are the prediction envelopes corresponding to two possible future behaviors—waiting by the crosswalk and crossing—identified by the MHP model. FRSP is the forward reachable set at t p assuming the pedestrian could have walked in any direction with a maximum speed of 2.5 m/s. The green line indicates observed ground truth trajectory up to time and the solid orange line indicates the ground truth trajectory after t p . The constant velocity predicts envelope e 1p based on the initial heading of the pedestrian. However, the pedestrian turns to cross at the crosswalk, which is captured by the prediction envelope e 3p of the MHP model.