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

Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure

Dynamic Occupancy Grid Prediction for Urban Autonomous Driving: A Deep Learning Approach with Fully Automatic Labeling

Long-term situation prediction plays a crucial role in the development of intelligent vehicles. A major challenge still to overcome is the prediction of complex downtown scenarios with multiple road users, e.g., pedestrians, bikes, and motor vehicles, interacting with each other. This contribution tackles this challenge by combining a Bayesian filtering technique for environment representation, and machine learning as long-term predictor. More specifically, a dynamic occupancy grid map is utilized as input to a deep convolutional neural network. This yields the advantage of using spatially distributed velocity estimates from a single time step for prediction, rather than a raw data sequence, alleviating common problems dealing with input time series of multiple sensors. Furthermore, convolutional neural networks have the inherent characteristic of using context information, enabling the implicit modeling of road user interaction. Pixel-wise balancing is applied in the loss function counteracting the extreme imbalance between static and dynamic cells. One of the major advantages is the unsupervised learning character due to fully automatic label generation. The presented algorithm is trained and evaluated on multiple hours of recorded sensor data and compared to Monte-Carlo simulation

Social influences on drivers in China

China is one of Asia’s many rapidly-motorising nations and recent increases in private-vehicle ownership have been coupled with an escalation in novice drivers. Several pieces of road safety legislation have been introduced in recent decades in China. While managing the legal aspects of road use is important, social influences on driver behaviour may offer alternative avenues to alter behaviour, particularly in a culture where such factors carry high importance. This paper reports qualitative research with Beijing drivers to investigate social influence factors that have, to date, received little attention in the literature. Findings indicated that family members, friends, and driving instructors appear influential on driver behaviour and that some newly licensed drivers seek additional assistance to facilitate the transition from learning to drive in a controlled environment to driving on the road in complex conditions. Strategies to avoid detection and penalties for inappropriate road use were described, many of which involved the use of a third person. These findings indicate potential barriers to implementing effective traffic enforcement and highlight the importance of understanding culturally-specific social factors relating to driver behaviour