Multimodal Hybrid Pedestrian: A Hybrid Automaton Model of Urban Pedestrian Behavior for Automated Driving Applications

Abstract

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. The resulting MHP 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. INDEX TERMS pedestrian trajectory prediction; pedestrian behavior recognition; human-centered robotics; advanced driver assistance systems (ADAS); autonomous vehicle navigation.

Variable-Drift Diffusion Models of Pedestrian Road-Crossing Decisions

“…The model may also be useful as a component in algorithms for real-time sensor data interpretation. Making use of models of pedestrian behavior in automated vehicle algorithms is an active area of research, but so far the models used have been relatively simplistic (Camara et al, 2020;Jayaraman et al, 2021;Kapania et al, 2019). Another important role of human behavior models in vehicle development is as agents in simulation environments for virtual testing (Behbahani et al, 2019;Camara et al, 2020;.…”

“…The majority of the research in this area has been observational (Lobjois & Cavallo, 2007;Schneemann & Gohl, 2016;Varhelyi, 1998), but some previous mathematical models exist. In the context of large-scale traffic simulation, logistic regression models have long been used to model pedestrian "gap acceptance" between vehicles in a stream of traffic (Brewer et al, 2006;Schroeder, 2008;Yannis et al, 2013), and the use of such models in automated vehicle algorithms has also been proposed (Jayaraman et al, 2021;Kapania et al, 2019). However, these models are limited to a discrete acceptance/rejection decision per gap, and do not account for the timing of roadcrossing decisions, which has implications for traffic flow and acceptance of automated vehicles (Dey et al, 2020;Lee et al, 2020;.…”

Variable-drift diffusion models of pedestrian road-crossing decisions

“…The model may also be useful as a component in algorithms for real-time sensor data interpretation. Making use of models of pedestrian behaviour in automated vehicle algorithms is an active area of research, but so far the mod-els used have been relatively simplistic [9,29,30]. Another important role of human behavior models in vehicle development is as agents in simulation environments for virtual testing [1,9,41].…”

“…The majority of the research in this area has been observational [35,59,67], but some previous mathematical models exist. In the context of large-scale traffic simulation, logistic regression models have long been used to model pedestrian 'gap acceptance' between vehicles in a stream of traffic [4,62,72], and the use of such models in automated vehicle algorithms has also been proposed [29,30]. However, these models are limited to a discrete acceptance/rejection decision per gap, and do not account for the timing of road-crossing decisions, which has implications for traffic flow and acceptance of automated vehicles [12,34,41].…”

HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling

“…by merging and pruning mode hypotheses [10], [11] or sampling from the prediction space [12], [13]. In the domain of trajectory prediction, the exponentially growing discrete space can be eased by expanding the discrete predictions at a few selected points [14] or accounting for the most probable intent [15]; however, they may not provide sufficient accuracy and coverage in a multi-modal problem.…”