2 research outputs found
Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks
Predicting pedestrians' trajectories is a crucial capability for autonomous
vehicles' safe navigation, especially in spaces shared with pedestrians.
Pedestrian motion in shared spaces is influenced by both the presence of
vehicles and other pedestrians. Therefore, effectively modelling both
pedestrian-pedestrian and pedestrian-vehicle interactions can increase the
accuracy of the pedestrian trajectory prediction models. Despite the huge
literature on ways to encode the effect of interacting agents on a pedestrian's
predicted trajectory using deep-learning models, limited effort has been put
into the effective selection of interacting agents. In the majority of cases,
the interaction features used are mainly based on relative distances while
paying less attention to the effect of the velocity and approaching direction
in the interaction formulation. In this paper, we propose a heuristic-based
process of selecting the interacting agents based on collision risk
calculation. Focusing on interactions of potentially colliding agents with a
target pedestrian, we propose the use of time-to-collision and the approach
direction angle of two agents for encoding the interaction effect. This is done
by introducing a novel polar collision grid map. Our results have shown
predicted trajectories closer to the ground truth compared to existing methods
(used as a baseline) on the HBS dataset.Comment: Accepted for publication as a conference paper in IEEE Intelligent
Transportation Systems Conference (ITSC), 202
Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review
Planning an autonomous vehicle's (AV) path in a space shared with pedestrians
requires reasoning about pedestrians' future trajectories. A practical
pedestrian trajectory prediction algorithm for the use of AVs needs to consider
the effect of the vehicle's interactions with the pedestrians on pedestrians'
future motion behaviours. In this regard, this paper systematically reviews
different methods proposed in the literature for modelling pedestrian
trajectory prediction in presence of vehicles that can be applied for
unstructured environments. This paper also investigates specific considerations
for pedestrian-vehicle interaction (compared with pedestrian-pedestrian
interaction) and reviews how different variables such as prediction
uncertainties and behavioural differences are accounted for in the previously
proposed prediction models. PRISMA guidelines were followed. Articles that did
not consider vehicle and pedestrian interactions or actual trajectories, and
articles that only focused on road crossing were excluded. A total of 1260
unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus
databases were identified in the search. 64 articles were included in the final
review as they met the inclusion and exclusion criteria. An overview of
datasets containing trajectory data of both pedestrians and vehicles used by
the reviewed papers has been provided. Research gaps and directions for future
work, such as having more effective definition of interacting agents in deep
learning methods and the need for gathering more datasets of mixed traffic in
unstructured environments are discussed.Comment: Published in IEEE Transactions on Intelligent Transportation System