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

Balance and agility in mountain bikers: a reliability and validity study on skills affecting control in mountain biking

Background Cycling is a popular recreational and competitive form of physical activity and method of transport. Cycling is broadly categorised as road cycling or mountain biking, and each form presents unique challenges and has different skill requirements. While cycling, in general, provides many benefits to both physical health and social behaviours, there are legitimate concerns about injuries related to both road and mountain cycling. Most of the available research presents the injury incidence in commuter or road cycling, with an apparent lack of evidence in mountain biking. The van Mechelen model of injury prevention outlines four stages in injury prevention research; the first stage investigates the extent of the injury and provides the basis on which the remaining stages depend. Based on the van Mechelen conceptual model, the broad aim of this thesis was to investigate acute injury epidemiology in mountain biking and the factors affecting bicycle control and falling. Investigations We performed a systematic review of the incidence of injury in mountain biking. Acute injury incidence ranged from 4% to 71% in cross-country mountain bike races. The causal indicators of bicycle control may include balance, agility and visual perception. In a pilot study, we developed novel tests to assess static bicycle balance and bicycle agility as measures of bicycle control. In the following study, we developed additional dynamic bicycle balance with four increasingly difficult levels. In this study, twenty-nine participants attended three days of repeated testing for reliability assessments of these tests. Participants also completed an outdoor downhill run. Performance in the balance tests were compared to performance in the outdoor downhill test to assess their ecological validity. All tests were assessed for reliability using typical error of measurement, standardised typical error, intraclass correlation coefficients, limits of agreement, effect sizes and repeated measures ANOVA's (with post hoc testing) analyses. The novel bicycle balance and agility were significantly associated with the performance in the outdoor downhill run (r=-0.51 to 0.78; p=0.01 to 0.0001). Cognitive and physical fatigue are factors that may contribute to loss of control of the bicycle. In our final study, we aimed to assess the effect of these factors on the performance in the novel tests. Rate of perceived exertion was significantly increased for all tests following physical fatigue (Cliff's d effect size= 0.27-0.40; p=0.001 to 0.037), but balance and agility performance were not affected. Cognitive fatigue had no effect on balance and agility performance. The fatigue induced in these protocols was insufficient to change performance in the bicycle-specific balance and agility tests. This indicates that either the fatigue protocols did not sufficiently replicate the fatigue experienced in mountain biking or that the tests are too blunt to be affected by the magnitude of fatigue in these protocols. Conclusion The overall incidence of injury in mountain biking is difficult to determine due to different injury definitions in the research. However, the available data clearly indicates an area of concern in sports and exercise medicine. We developed novel tests to assess the skill components of balance and agility on a mountain bike. The novel bicycle-specific tests are robust assessments of mountain biking performance and can be applied in clinical and research environments to determine bicycle control. Cognitive and physical fatigue did not affect performance on these novel tests. Based on the overall findings of our studies, we recommend that further research is conducted on the epidemiology of mountain biking injuries. The effect of fatigue on the novel tests needs to be investigated further using a combination of physical and cognitive fatigue

Supervised learning and inference of semantic information from road scene images

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

Supervised learning and inference of semantic information from road scene images

Premio Extraordinario de Doctorado de la UAH en el año académico 2013-2014Nowadays, vision sensors are employed in automotive industry to integrate advanced functionalities that assist humans while driving. However, autonomous vehicles is a hot field of research both in academic and industrial sectors and entails a step beyond ADAS. Particularly, several challenges arise from autonomous navigation in urban scenarios due to their naturalistic complexity in terms of structure and dynamic participants (e.g. pedestrians, vehicles, vegetation, etc.). Hence, providing image understanding capabilities to autonomous robotics platforms is an essential target because cameras can capture the 3D scene as perceived by a human. In fact, given this need for 3D scene understanding, there is an increasing interest on joint objects and scene labeling in the form of geometry and semantic inference of the relevant entities contained in urban environments. In this regard, this Thesis tackles two challenges: 1) the prediction of road intersections geometry and, 2) the detection and orientation estimation of cars, pedestrians and cyclists. Different features extracted from stereo images of the KITTI public urban dataset are employed. This Thesis proposes a supervised learning of discriminative models that rely on strong machine learning techniques for data mining visual features. For the first task, we use 2D occupancy grid maps that are built from the stereo sequences captured by a moving vehicle in a mid-sized city. Based on these bird?s eye view images, we propose a smart parameterization of the layout of straight roads and 4 intersecting roads. The dependencies between the proposed discrete random variables that define the layouts are represented with Probabilistic Graphical Models. Then, the problem is formulated as a structured prediction, in which we employ Conditional Random Fields (CRF) for learning and convex Belief Propagation (dcBP) and Branch and Bound (BB) for inference. For the validation of the proposed methodology, a set of tests are carried out, which are based on real images and synthetic images with varying levels of random noise. In relation to the object detection and orientation estimation challenge in road scenes, this Thesis goal is to compete in the international challenge known as KITTI evaluation benchmark, which encourages researchers to push forward the current state of the art on visual recognition methods, particularized for 3D urban scene understanding. This Thesis proposes to modify the successful part-based object detector known as DPM in order to learn richer models from 2.5D data (color and disparity). Therefore, we revisit the DPM framework, which is based on HOG features and mixture models trained with a latent SVM formulation. Next, this Thesis performs a set of modifications on top of DPM: I) An extension to the DPM training pipeline that accounts for 3D-aware features. II) A detailed analysis of the supervised parameter learning. III) Two additional approaches: "feature whitening" and "stereo consistency check". Additionally, a) we analyze the KITTI dataset and several subtleties regarding to the evaluation protocol; b) a large set of cross-validated experiments show the performance of our contributions and, c) finally, our best performing approach is publicly ranked on the KITTI website, being the first one that reports results with stereo data, yielding an increased object detection precision (3%-6%) for the class 'car' and ranking first for the class cyclist

What It’s Like to Ride a Bike: Understanding Cyclist Experiences

Cyclists can make important contributions to transport policy, if only we ask them. This thesis explores how people experience cycling in three case study cities – Perth, Melbourne and Utrecht. Cyclists were recruited for semi-structured and go-along interviews. The key findings indicate that the combination of traditional and mobile methods yield valuable information for developing understandings of the embodied experience of cycling, which can be used to inform policy and guide the creation of sustainable cities

The embodied dimensions of road cycling and the formation of gendered cycling identities

This thesis aims to offer a better understanding as to why road cycling remains one of Australia’s most popular leisure activities, despite a reported 38 road cyclists killed annually and another 12,000 seriously injured (AIHW, 2019). By investigating cycling sensations of the body and cycling, this thesis responds to calls from feminist geographical scholarship to embraced embodied approaches. Building on feminist readings of the work of Deleuze and Guattari (1987), this thesis offers the concepts of the ‘cycling assemblage’ and ‘cycling refrain’ to help rethink the relationship between mobility, subjectivities and place. Two important implications arise. First, attention is drawn to how road cycling is always more than a human achievement through the involvement of the topography, weather, bikes, clothes, light and so on. Second, identification as a road cyclist is never fixed or pre-existing, rather is always emerging through the sensations felt during the coming together of ideas and materials on the move. Insights into becoming a road cyclist build on methodological arguments that call for a sensory ethnography. Cycling sensory ethnographies designed for this project combined semi-structured interviews with go-alongs and qualitative geographic information systems. 27 people consented to participate. All identified as leisure road cyclists and lived in the car-dominated small city of Wollongong, on the east coast of New South Wales, Australia. The sensory analysis involved mapping affective moments that provide important insights into the gendered dynamics of leisure cycling and self-tracking technologies, the embodied dimensions of mobility justice, and rethinking wellbeing through cycling as a more than human achievement. The thesis concludes by highlighting contributions to the academy and future research

Subjective risk and memory for driving situations

This thesis explores the relationship between subjective risk when driving and drivers' subsequent memory for everyday driving situations. Relationships are considered in the context of the wider literature on arousal and memory. In the first study subjects drove a set route around Cambridge giving verbal risk ratings; they then performed an unexpected free recall task. Drivers tended to recall situations which they had previously rated as risky. A series of laboratory studies explored this result. In these studies subjects watched films of actual driving situations in a simulator and were given subsequent recognition tests. In the first laboratory study subjective risk was only associated with improved recognition sensitivity for the most potentially dangerous situations. In generally safe situations feelings of risk appeared to impair recognition. These results were replicated in two further laboratory studies using different judgment tasks and stimuli. These results could be explained by subjective risk causing the focusing of attention in driving with a consequent enhancement of memory for central details at the expense of memory for peripheral details. To directly test the attention focusing hypothesis a laboratory study defined central information with respect to risk in driving situations. Then an on-road study found that drivers did indeed recall more central details than would be expected from risky situations. There thus appear to be two relationships between subjective risk and memory in driving. The first is an overall tendency for subjects to recall risky situations. This is assumed to be largely because such events are rare and distinctive. The second is a tendency for subjects to recall central details of risky situations at the expense of peripheral details. This is consistent with recent studies on attention focusing in eyewitness testimony. Some implications of these results for eyewitness testimony and for the psychology of driving are considered

Pedestrian and cyclist detection and intent estimation for autonomous vehicles: A survey

© 2019 by the authors. As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN), Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated