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

Efficient Behavior-aware Control of Automated Vehicles at Crosswalks using Minimal Information Pedestrian Prediction Model

For automated vehicles (AVs) to reliably navigate through crosswalks, they need to understand pedestrians’ crossing behaviors. Simple and reliable pedestrian behavior models aid in real-time AV control by allowing the AVs to predict future pedestrian behaviors. In this paper, we present a Behavior aware Model Predictive Controller (B-MPC) for AVs that incorporates long-term predictions of pedestrian crossing behavior using a previously developed pedestrian crossing model. The model incorporates pedestrians’ gap acceptance behavior and utilizes minimal pedestrian information, namely their position and speed, to predict pedestrians’ crossing behaviors. The BMPC controller is validated through simulations and compared to a rule-based controller. By incorporating predictions of pedestrian behavior, the B-MPC controller is able to efficiently plan for longer horizons and handle a wider range of pedestrian interaction scenarios than the rule-based controller. Results demonstrate the applicability of the controller for safe and efficient navigation at crossing scenarios.Automotive Research Center (ARC) at the University of Michigan, with funding from government contract DoD-DoA W56HZV14-2-0001, through the U.S. Army Combat Capabilities Development Command (CCDC) /Ground Vehicle Systems Center (GVSC).National Science FoundationPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154113/1/Jayaraman_etal_ACC_2020__Behavior_aware_controller_final.pdfDescription of Jayaraman_etal_ACC_2020__Behavior_aware_controller_final.pdf : MainFil

Combining Bayesian and AI approaches for Autonomous Driving

International audienceInvited Keynote Talk. This talk addresses the exciting new concept of Autonomous Driving, as well as the technical questions and solutions associated with it. Emphasis will be placed on the scientific and technological challenges associated with issues of embedded perception, understanding of complex dynamic scenes and real-time driving decision-making. It will be shown how these problems can be tackled using Bayesian Perception, Artificial Intelligence and Machine Learning approaches. The talk will be illustrated using results obtained by Inria Grenoble Rhône-Alpes (France) in the scope of several R&D projects conducted in collaboration with IRT Nanoelec (French Technological Research Institute) and with several industrial companies such as Toyota or Renault

Modeling Cooperative Navigation in Dense Human Crowds

For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used hand-crafted functions based on proximity to model human-human and human-robot interactions. However, these approaches can only model simple interactions and fail to generalize for complex crowded settings. In this paper, we develop an approach that models the joint distribution over future trajectories of all interacting agents in the crowd, through a local interaction model that we train using real human trajectory data. The interaction model infers the velocity of each agent based on the spatial orientation of other agents in his vicinity. During prediction, our approach infers the goal of the agent from its past trajectory and uses the learned model to predict its future trajectory. We demonstrate the performance of our method against a state-of-the-art approach on a public dataset and show that our model outperforms when predicting future trajectories for longer horizons.Comment: Accepted at ICRA 201