Predict Vehicle Collision by TTC From Motion Using a Single Video Camera

The objective of this paper is the instantaneous computation of time-to-collision (TTC) for potential collision only from the motion information captured with a vehicle borne camera. The contribution is the detection of dangerous events and degree directly from motion divergence in the driving video, which is also a clue used by human drivers. Both horizontal and vertical motion divergence are analyzed simultaneously in several collision sensitive zones. The video data are condensed to the motion profiles both horizontally and vertically in the lower half of the video to show motion trajectories directly as edge traces. Stable motion traces of linear feature components are obtained through filtering in the motion profiles. As a result, this avoids object recognition and sophisticated depth sensing in prior. The fine velocity computation yields reasonable TTC accuracy so that a video camera can achieve collision avoidance alone from the size changes of visual patterns. We have tested the algorithm for various roads, environments, and traffic, and shown results by visualization in the motion profiles for overall evaluation

Detection and Simulation of Dangerous Human Crowd Behavior

Tragically, gatherings of large human crowds quite often end in crowd disasters such as the recent catastrophe at the Loveparade 2010. In the past, research on pedestrian and crowd dynamics focused on simulation of pedestrian motion. As of yet, however, there does not exist any automatic system which can detect hazardous situations in crowds, thus helping to prevent these tragic incidents. In the thesis at hand, we analyze pedestrian behavior in large crowds and observe characteristic motion patterns. Based on our findings, we present a computer vision system that detects unusual events and critical situations from video streams and thus alarms security personnel in order to take necessary actions. We evaluate the system’s performance on synthetic, experimental as well as on real-world data. In particular, we show its effectiveness on the surveillance videos recorded at the Loveparade crowd stampede. Since our method is based on optical flow computations, it meets two crucial prerequisites in video surveillance: Firstly, it works in real-time and, secondly, the privacy of the people being monitored is preserved. In addition to that, we integrate the observed motion patterns into models for simulating pedestrian motion and show that the proposed simulation model produces realistic trajectories. We employ this model to simulate large human crowds and use techniques from computer graphics to render synthetic videos for further evaluation of our automatic video surveillance system

Keeping that Doggie in the (Car) Window Safe: Recommendations for Driving with Canine Companions

Part I of this article uses the empirical research of experts (animal behaviorists, ethologists, and cynologists) to explain reasons canine companions and their humans love riding together. It also highlights the potential risks when dogs distract drivers. Acknowledging that only a few studies exist, it provides whatever statistics are available and posits that data strongly suggest dogs who are roaming free in a car cause or at least contribute to many accidents. This means, in addition to drivers and passengers in other vehicles and even pedestrians, these animals represent a significant potential danger to themselves and occupants of their car in one of two respects. First, they can distract the driver in any number of ways and cause or contribute to an accident. Second, they can become deadly projectiles if there is a collision (or even just the need to stop short). Part II tackles the important question of how to significantly decrease the number of canine-involved accidents. This section analyzes the relevant cases and laws in the United States. It briefly contrasts what is happening in other places in the world, including the United Kingdom where it is illegal to drive with an unrestrained dog. Doing so actually permits insurance companies to reject any resulting claims. Then, after conceding the impossibility of eliminating all such mishaps, Part III explores ways to protect human and nonhuman animals when a crash occurs. These include the need to collect information about collisions where dogs distracted the driver similar to what was done when deciding that adults need to wear seat belts and children must be in safety seats. Finally, the Conclusion provides several recommendations

Visual Vertigo, Motion Sickness and Disorientation in vehicles

The normal vestibular system may be adversely affected by environmental challenges which have characteristics that are unfamiliar or ambiguous in the patterns of sensory stimulation they provide. A disordered vestibular system lends susceptibility even to quotidian environmental experiences as the sufferer becomes dependent on potentially misleading, non-vestibular sensory stimuli. In both cases the sequela may be dizziness, incoordination, imbalance and unpleasant autonomic responses. Many forms of visual environmental motion, particularly busy places such as supermarkets, readily induce inappropriate sensations of sway or motion and imbalance referred to as visual vertigo. All people with intact vestibular function can become motion sick although individual susceptibility varies widely and is partially determined by inheritance. Motorists learn to interpret sensory stimuli in the context of the car stabilised by its suspension and guided by steering. A type of motorist disorientation occurs in some individuals that develop a heightened awareness of false perceptions of car orientation, readily experiencing stereotypical symptoms of threatened rolling over on corners and veering on open highways or in streaming traffic. This article discusses the putative mechanisms, consequences and approach to managing patients with visual vertigo, motion sickness and motorist disorientation syndrome in the context of chronic dizziness and motion sensitivity

The Effects of Road Marking Patterns on Simulated Driving Speed and Lane Position

人間科学部人間科学科行動学科目安全行動学学研究分野：卒業論

Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2013 Annual Conference:Human Factors: sustainable life and mobility

On the occasion of the 2013 Meeting in Torino, Ital

Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2013 Annual Conference:Human Factors: sustainable life and mobility

On the occasion of the 2013 Meeting in Torino, Ital