An intelligent pedestrian device: social, psychological and other issues of feasibility

An Intelligent Pedestrian Device (IPD) is a new concept in pedestrian safety. It is defined as a microprocessor based information device which detects the approach of oncoming vehicles and informs the pedestrian whether or not it is safe to cross. IPDs could be portable or fixed to a roadside station. They could help reduce pedestrian accidents, which cost £2681 million in the UK in 1994. This study aims to assess whether the concept is socially acceptable and what the design criteria might be. A study of social acceptance involved group interviews of 5-10 participants with 84 pedestrians in five categories: adults aged 18-60, elderly aged 65+, visually restricted, parents of children aged 5-9 and children aged 10-14. The results suggest that vulnerable pedestrians are more positive about the device than the more able-bodied. Theories that may help explain this are discussed and it is concluded that, with education and marketing, the IPD could gain a degree of social acceptance. Observation of more than 900 pedestrian crossing movements at four different sites showed a range of behaviours, and that people often take risks in order to reduce delay. IPDs will require pedestrians to change some of their behaviours, especially those that are risky. Legal acceptance will demand high levels of costly product research and development, and a portable device will not be technologically feasible until well into the next century. However, the wider social benefits of IPDs may be worth the costs. An outline of design criteria for basic and sophisticated portable IPDs is given, and alternative functions are suggested. It is recommended that further work concentrate on developing software and hardware for fixed modes of IPD. It is concluded that, ultimately, acceptance will probably depend on whether Government decides that the IPD has a place in the road environment of the future

Quality Of Service Measures At Signalized Intersections

The concept of using qualitative measures to describe the quality of service at signalized intersections provided by different designs and controls has been discussed in numerous conferences. Such measures may include driver\u27s comfort, convenience, anxiety, and preferences. The primary objective of this study was to demonstrate the feasibility of using the University of Central Florida\u27s interactive driving simulator to execute several scenarios involving different unusual design and operation practices to measure the quality of service at a signalized intersection. This thesis describes the scenarios, the experiments conducted, the data collected, and analysis of results. Signalized intersections with 3 types of characteristic features were identified for this study. They included 1. A lane dropping on the downstream side of the intersection 2. Misalignment of traffic lanes between the approach and downstream side 3. Shared left turn and through traffic lane or separate lanes for each approaching the intersection The experimental phase consisted of a brief orientation session to get acclimated to the driving simulator followed by two driving scenarios presented to all subjects. Each scenario consisted of a drive through an urban section of the simulator\u27s visual data base where each subject encountered a Type 1, 2 and 3 intersections. A total of 40 subjects, 25 males and 15 females were recruited for the experiment. Data logging at 60 Hz for each scenario consisted of time-stamped values of x-position and y-position of the simulator vehicle, steering, accelerator and brake inputs by the driver, and vehicle speed. After the experiment a questionnaire soliciting opinions and reactions about each intersection was administered. Simulator experiment results showed that there was a significant difference between the merge lengths for the two cases of Type 1 intersection (lane drop on the downstream side of the intersection). For Type 2 intersection (misalignment of traffic lanes between the approach and downstream side) there was a considerable difference between the average paths followed by subjects for the two cases. For Type 3 intersection (shared left and through traffic lane approaching the intersection) the simulator experiment supported the fact that people get frustrated when trapped behind a left turning vehicle in a joint left and through lane intersection and take evasive actions to cross the intersection as soon as possible

A High-Fidelity VR Simulation Study: Do External Warnings Really Improve Pedestrian Safe Crossing Behavior?

To better communicate with pedestrians, adding external displays to autonomous vehicles (AVs) has been proposed as a potential communication method to encourage safe crossing behavior by pedestrians. Whereas, most researchers have conducted intercept interviews, lab studies, or simulation studies to explore the efficacy of these displays, these approaches only studied crossing intention but did not explore crossing behavior. We developed a high-fidelity virtual reality scenario where participants could demonstrate actual crossing behavior within an adequately replicated real-world street. We simulated a local street with scalability of the real world in a VR environment, conducted an experiment in an empty space large enough for participants to move across the road in the VR environment. A mixed-method approach assessed attitudinal and behavioral interactions with potential warning patterns. The results showed that the warning patterns contributed significantly to pedestrians’ perceptual vigilance, as in past studies, but safer crossing behavior was not observed. This suggests that crossing intention measures may not be an adequate substitute for behavioral measures of crossing

Factors affecting blind mobility

This thesis contains a survey of the mobility problems of blind people, experimental analysis and evaluation of these problems and suggestions for ways in which the evaluation of mobility performance and the design of mobility aids may be improved. The survey revealed a low level of mobility among blind people, with no significant improvement since a comparable survey in 1967. A group of self taught cane users were identified and their mobility was shown to be poor or potentially dangerous. Existing measures of mobility were unable to detect improvements in performance above that achieved by competent long cane users. By using newly devised measures of environmental awareness and of gait, the advantages of the Sonic Pathfinder were demonstrated. Existing measures of psychological stress were unsatisfactory. Heart rate is affected by physical effort and has been shown to be a poor indicator of moment-to-moment stress in blind mobility. Analysis of secondary task errors showed that they occurred while obstacles were being negotiated. They did not measure stress due to anticipation of obstacles or of danger. In contrast, step length, stride time and particularly speed all show significant anticipatory effects. The energy expended in walking a given distance is least at the walker's preferred speed. When guided, blind people walk at this most efficient pace. It is therefore suggested that the ratio of actual to preferred speed is the best measure of efficiency in mobility. Both guide dogs and aids which enhance preview allow pedestrians to walk at, or close to, their preferred speed. Further experiments are needed to establish the extent to which psychological stress is present during blind mobility, since none of the conventional measures, such as heart rate and mood checklists show consistent effects. Walking speed may well prove to be the most useful measure of such stress

C-MobILE D6.3: Impact assessment on users and key stakeholders

The main scope of this document is to present the subjective evaluation results gained from surveying users who actually experienced the services deployed in the C-MobILE project. The assessment focuses on the performance of the systems at service and at app level. In order to examine the quality of the implementations, common criteria such as (perceived) usefulness, (perceived) performance, reliability, and usability are taken into account

Early Design Evaluation of See-Through Automotive A-pillar Concepts Using Digital Human Modeling and Mixed Reality Techniques

Pillars in modern vehicles provide structural integrity by surrounding the windows and joining the roof with the frame. Although they protect the driver and passengers, A-pillars also create obstruction zones by limiting drivers' forward field of view leading to unforeseeable accidents or mishaps. We argue that providing a better pillar design that embodies a human-centered approach could prevent such incidents. In this research, we propose an early design framework that integrates methods of generative design (GD) and digital human modeling (DHM) to demonstrate a proof-of-concept study evaluated using Mixed Reality (MR). The framework assesses whether the concept design approach improves drivers' overall visibility of traffic objects that would otherwise be obscured. The overarching goal of this study is to increase user feedback by creating a human-in-the-loop framework that supports the preliminary design activities early in design before committing to costly physical prototypes. This study demonstrates how designers can quantify obstruction zones and gather user feedback to gain a deeper insight into the A-pillar obstruction problem via DHM and MR early in the design process

Pedestrian Detection by Computer Vision.

This document describes work aimed at determining whether the detection, bycomputer vision, of pedestrians waiting at signal-controlled road crossings could bemade sufficiently reliable and affordable, using currently available technology, so asto be suitable for widespread use in traffic control systems.The work starts by examining the need for pedestrian detection in traffic controlsystems and then goes onto look at the specific problems of applying a vision systemto the detection task. The most important distinctive features of the pedestriandetection task addressed in this work are:• The operating conditions are an outdoor environment with no constraints onfactors such as variation in illumination, presence of shadows and the effects ofadverse weather.• Pedestrians may be moving or static and are not limited to certain orientations orto movement in a single direction.• The number of pedestrians to be monitored is not restricted such that the visionsystem must cope with the monitoring of multiple targets concurrently.• The background scene is complex and so contains image features that tend todistract a vision system from the successful detection of pedestrians.• Pedestrian attire is unconstrained so detection must occur even when details ofpedestrian shape are hidden by items such as coats and hats.• The camera's position is such that assumptions commonly used by vision systemsto avoid the effects of occlusion, perspective and viewpoint variation are not valid.•The implementation cost of the system, in moderate volumes, must be realistic forwidespread installation.A review of relevant prior art in computer vision with respect to the above demands ispresented. Thereafter techniques developed by the author to overcome thesedifficulties are developed and evaluated over an extensive test set of image sequencesrepresentative of the range of conditions found in the real world.The work has resulted in the development of a vision system which has been shown toattain a useful level of performance under a wide range of environmental andtransportation conditions. This was achieved, in real-time, using low-cost processingand sensor components so demonstrating the viability of developing the results of thiswork into a practical detector

Scientific, Technical, and Forensic Evidence

Materials from the conference on Scientific, Technical, and Forensic Evidence held by UK/CLE in February 2002

A Context Aware Classification System for Monitoring Driver’s Distraction Levels

Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected. The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected. This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner