Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

MIXED-USE SAFETY ON RURAL FACILITIES IN THE PACIFIC NORTHWEST: Consideration of Vehicular, Non-Traditional, and Non-Motorized Users

In the United States, one in 12 households do not own a personal automobile and approximately 13% of those who are old enough to drive do not. Trips by these individuals are being made in one of many other possible modes, creating the need to “share space” between many forms of travel. The goal of this project is to: improve safety and minimize the dangers for all transportation mode types while traveling in mixed-use environments on rural facilities through the development and use of engineering and education safety measures. To that end, this report documents three specific efforts by the project team. First, a comprehensive literature review of mixed-use safety issues with consideration of non-motorized and non-traditional forms of transportation. Second, a novel analysis of trauma registry data. Third, development, execution and analysis of the Pacific Northwest Transportation Survey geared toward understanding safety perceptions of mixed-use users. Most notably, findings indicate that ATVs (and similar non-traditional-type vehicles) are used on or near roads 24% of the time and snowmachines are used on or near roads 23% of the time. There are significantly more (twice as many) ATV-related on-road traumas in connected places than isolated places in Alaska and three times more traumas in highway connected places than in secondary road connected places. Comparably, bicycles had 449 on-road traumas between 2004 and 2011 whereas ATVs had 352 on-road traumas. Users of all modes who received formalized training felt safer in mixed-use environments than those who reported having no training at all

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

Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control

A Robust Scenario MPC Approach for Uncertain Multi-modal Obstacles

Motion planning and control algorithms for autonomous vehicles need to be safe, and consider future movements of other road users to ensure collision-free trajectories. In this letter, we present a control scheme based on Model Predictive Control (MPC) with robust constraint satisfaction where the constraint uncertainty, stemming from the road users\u27 behavior, is multimodal. The method combines ideas from tube-based and scenario-based MPC strategies in order to approximate the expected cost and to guarantee robust state and input constraint satisfaction. In particular, we design a feedback policy that is a function of the disturbance mode and allows the controller to take less conservative actions. The effectiveness of the proposed approach is illustrated through two numerical simulations, where we compare it against a standard robust MPC formulation