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

Belief State Planning for Autonomously Navigating Urban Intersections

Urban intersections represent a complex environment for autonomous vehicles with many sources of uncertainty. The vehicle must plan in a stochastic environment with potentially rapid changes in driver behavior. Providing an efficient strategy to navigate through urban intersections is a difficult task. This paper frames the problem of navigating unsignalized intersections as a partially observable Markov decision process (POMDP) and solves it using a Monte Carlo sampling method. Empirical results in simulation show that the resulting policy outperforms a threshold-based heuristic strategy on several relevant metrics that measure both safety and efficiency.Comment: 6 pages, 6 figures, accepted to IV201

Actors and factors - bridging social science findings and urban land use change modeling

Recent uneven land use dynamics in urban areas resulting from demographic change, economic pressure and the cities’ mutual competition in a globalising world challenge both scientists and practitioners, among them social scientists, modellers and spatial planners. Processes of growth and decline specifically affect the urban environment, the requirements of the residents on social and natural resources. Social and environmental research is interested in a better understanding and ways of explaining the interactions between society and landscape in urban areas. And it is also needed for making life in cities attractive, secure and affordable within or despite of uneven dynamics.\ud The position paper upon “Actors and factors – bridging social science findings and urban land use change modeling” presents approaches and ideas on how social science findings on the interaction of the social system (actors) and the land use (factors) are taken up and formalised using modelling and gaming techniques. It should be understood as a first sketch compiling major challenges and proposing exemplary solutions in the field of interest

Improving Automated Driving through Planning with Human Internal States

This work examines the hypothesis that partially observable Markov decision process (POMDP) planning with human driver internal states can significantly improve both safety and efficiency in autonomous freeway driving. We evaluate this hypothesis in a simulated scenario where an autonomous car must safely perform three lane changes in rapid succession. Approximate POMDP solutions are obtained through the partially observable Monte Carlo planning with observation widening (POMCPOW) algorithm. This approach outperforms over-confident and conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP baselines, POMCPOW typically cuts the rate of unsafe situations in half or increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085

Supporting arts and enterprise skills in communities through creative engagement with the local area

The project proposes a framework and methodology of artistic and creative social intervention that empowers and supports engagement with communities of young people affected by change in their local environment. This is a UK Arts and Humanities Research Council funded Knowledge Transfer Fellowship aimed at building new and innovative models of creative community engagement and collaboration. The project supports active citizenship among young people by facilitating social capacity building through enterprise structures and transferring the creative lead in socially responsive arts projects to those in need of empowerment. The initial action research project is utilising an arts and enterprise participation model to create self-branded commodities that will give a role to young people within a wider, community driven, gun crime reduction and social cohesion programme. The model seeks to sustain the commitment of those participating by focussing on metrics and benchmarks that young people in the project can own and influence. The blend of creative agendas and enterprise goals provides a breadth of purpose and opportunity, linking outputs to specific environmental and social impacts. The project evidences the role and function of arts media in multi-strand learning and participation projects. As educational policy and practice (14+ age range) in the UK moves more towards action based learning for transferable life skills, the project provides a methodology emphatic of team and collaborative process, individual responsibility and creativity. The process develops ownership and shared responsibility in relation to community initiatives; fostering fresh creativity and a diversity of approach in the exploration of social, physical and racial issues arising from economic disadvantage. The knowledge transfer process is targeting a toolkit relating to multi-agency project working, creative research and action learning, empowerment and applied social arts practices