This paper describes the recent research on the enhancement of pedestrian safety to help develop a better understanding of the nature, issues, approaches, and challenges surrounding the problem. It presents a comprehensive review of research efforts underway dealing with pedestrian safety and collision avoidance. The importance of pedestrian protection is emphasized in a global context, discussing the research programs and efforts in various countries. Pedestrian safety measures, including infrastructure enhancements and passive safety features in vehicles, are described, followed by a systematic description of active safety systems based on pedestrian detection using sensors in vehicle and infrastructure. The pedestrian detection approaches are classified according to various criteria such as the type and configuration of sensors, as well as the video cues and classifiers used in detection algorithms. It is noted that collision avoidance not only requires detection of pedestrians but also requires collision prediction using pedestrian dynamics and behavior analysis. Hence, this paper includes research dealing with probabilistic modeling of pedestrian behavior for predicting collisions between pedestrians and vehicles.

Abstract-This paper presents investigations into the role of computer-vision technology in developing safer automobiles. We consider vision systems, which cannot only look out of the vehicle to detect and track roads and avoid hitting obstacles or pedestrians but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computervision technology for safer automobiles is presented. We will consider three main components of the system: environment, vehicle, and driver. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. This paper includes a discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent, and activity patterns of drivers.

This paper gives a survey of recent research on pedestrian collision avoidance systems. Collision avoidance not only requires detection of pedestrians, but also collision prediction using pedestrian dynamics and behavior analysis. The paper reviews various approaches based on cues such as shape, motion, and stereo used for detecting pedestrians from visible as well as non-visible light sensors. This is followed by the study of research dealing with probabilistic modeling of pedestrian behavior for predicting collisions between pedestrian and vehicle. The literature review is also condensed in tabular form for quick reference.

Pedestrian protection is an essential component of driver assistance systems. A pedestrian protection system should be able to predict the possibility of collision after detecting the pedestrian, and it is important to consider all the cues available in order to make that prediction. The direction in which the pedestrian is facing is one such cue that could be used in predicting where the pedestrian may move in future. This paper describes a novel approach to determine the pedestrian's orientation using Support Vector Machine (SVM) based scheme. Instead of providing a hard decision, this scheme estimates the discrete probability distribution of the orientation. A Hidden Markov Model (HMM) is used to model the transitions between orientations over time and the orientation probabilities are integrated over time to get a more reliable estimate of orientation. Experiments showing the performance of estimating orientations are described to show the promise of the approach.

No abstract