FORECASTING OF SUDDEN PEDESSTRAIN CROSSING FOR SAFE DRIVING DURING NIGHTS BY HIGH INTENSITY NIGHT VISION CAMERA

Sudden pedestrian crossing (SPC) is the major reason for pedestrian-vehicle crashes. In this paper, we focus on detecting SPCs at night for supporting an advanced driver assistance system using a far-infrared (FIR) camera mounted on the front-roof of a vehicle. Although the thermal temperature of the road is similar or higher than that of the pedestrians during summer nights, many previous researches have focused on pedestrian detection during the winter, spring, or autumn seasons. However, our research concentrates on SPC during the hot summer season because the number of collisions between pedestrians and vehicles in Korea is higher at that time than during the other seasons. For real-time processing, we first decide the optimal levels of the image scaling and search area. We then use our proposed method for detecting virtual reference lines that are associated with road segmentation without using color information, and change these lines according to the turning direction of the vehicle. Pedestrian detection is conducted using a cascade random forest with low-dimensional Haar-like features and oriented center symmetric-local binary patterns. The SPC prediction is assessed based on the likelihood and the spatiotemporal features of the pedestrians, such as their overlapping ratio with virtual reference lines, as well as the direction and magnitude of each pedestrian’s movement. The proposed algorithm was successfully applied to various pedestrian dataset captured by an FIR camera, and the results show that its SPC detection performance is better than those of other methods

MS-DETR: Multispectral Pedestrian Detection Transformer with Loosely Coupled Fusion and Modality-Balanced Optimization

Multispectral pedestrian detection is an important task for many around-the-clock applications, since the visible and thermal modalities can provide complementary information especially under low light conditions. Most of the available multispectral pedestrian detectors are based on non-end-to-end detectors, while in this paper, we propose MultiSpectral pedestrian DEtection TRansformer (MS-DETR), an end-to-end multispectral pedestrian detector, which extends DETR into the field of multi-modal detection. MS-DETR consists of two modality-specific backbones and Transformer encoders, followed by a multi-modal Transformer decoder, and the visible and thermal features are fused in the multi-modal Transformer decoder. To well resist the misalignment between multi-modal images, we design a loosely coupled fusion strategy by sparsely sampling some keypoints from multi-modal features independently and fusing them with adaptively learned attention weights. Moreover, based on the insight that not only different modalities, but also different pedestrian instances tend to have different confidence scores to final detection, we further propose an instance-aware modality-balanced optimization strategy, which preserves visible and thermal decoder branches and aligns their predicted slots through an instance-wise dynamic loss. Our end-to-end MS-DETR shows superior performance on the challenging KAIST, CVC-14 and LLVIP benchmark datasets. The source code is available at https://github.com/YinghuiXing/MS-DETR

The Influence of Weather and Climate Change on Pedestrian Safety

Although walking is one of the most sustainable means of transportation with a number of related health benefits to human life, in a number of regions, walking on roads can lead to increased chances of injury and death from a collision. Due to the dramatic growth in motor vehicle usage, pedestrians are easily susceptible to collisions with these vehicles. This problem is even more enhanced when inclement weather occurs. Pedestrian vulnerability is more heightened during weather hazards, since such hazards tend to increase the risk of collisions. The influence of weather hazards on motor vehicle safety and collision risk is established in the road safety literature, but fewer research exists on the safety and risk of pedestrian-vehicle collisions during inclement weather. Therefore, the first objective of the thesis is to estimate the relative risk of collisions during rainfall in two urban Canadian Regions: the Greater Toronto Area and Greater Vancouver. The second objective is predictive in nature using a combination of the relative risk estimates and available climate models to understand the possible influence of climate change on pedestrian safety in both regions by the mid-century. The results indicated that present-day relative risk of collisions during rainfall relative to dry weather is higher for pedestrians in Vancouver than Toronto, however, at a finer temporal scale the relative risk is almost the same for both regions. By mid-century, the results of the climate modeling exercise estimate an increase in mean annual rain days for both regions, where much of the increase is for light rainfall days. This additional increase in mean annual rain days will increase pedestrian collisions each year by a small amount in Toronto and by a slightly higher amount in Vancouver. In both regions, collisions occurring at public intersections and casualties have significant increase in risk during rainfall. Moreover, with climate change, the additional collisions are belonging to public intersection collisions and casualties in Toronto and Vancouver, respectively. Evidently, most of the increase in risk and additional collisions by mid-century is attributable to moderate and heavy rainfall periods. Therefore, safety interventions should consider the impact of intense rainfall on pedestrian safety in the present and future possible climate

Accident causation and pre-accidental driving situations. Part 2. In-depth accident causation analysis

WP2 of the European Project TRACE is concerned with “Types of Situations” to analyse the causation of road traffic accidents from the pre-accidental driving situation point of view. Four complementary situations were defined: stabilized situations, intersection, specific manoeuvre and degradation scenario. To reach this objective, the analysis is based on a common methodology composed on 3 steps: the “descriptive analysis” which from general statistics will allow to identify among the studied situations those them relevant and to give their characteristics, the “in-depth analysis” allowing to obtain accident causes from the generic description of the problems identified in the previous step and the risk analysis identifying the risk of being involved in an accident taking into account the results obtained from the ‘in–depth’ level. This report is dedicated to the identification of the accident causes analysed for the pre-accidental driving situation point of view, i.e. the circumstances in which the driver is involved just prior the accident. This analysis has been conducted from the scenarios identified for each type of situation during the descriptive analysis realized in a first part (Report D2.1: Accident causation and pre-accidental driving situations. Part 1. Overview and general statistics). These results are based on the study of disaggregated data (in-depth accidents collection databases) available via WP8 in TRACE. With the identification of the main causes and contributing factor, the aspect related to the human functional failure has been taken into account. This innovative concept studied in TRACE WP5, has been used here in order to have a more complete overview of the problems in working on each road users involved in the accident and not only on the whole accident

Situational Awareness Enhancement for Connected and Automated Vehicle Systems

Recent developments in the area of Connected and Automated Vehicles (CAVs) have boosted the interest in Intelligent Transportation Systems (ITSs). While ITS is intended to resolve and mitigate serious traffic issues such as passenger and pedestrian fatalities, accidents, and traffic congestion; these goals are only achievable by vehicles that are fully aware of their situation and surroundings in real-time. Therefore, connected and automated vehicle systems heavily rely on communication technologies to create a real-time map of their surrounding environment and extend their range of situational awareness. In this dissertation, we propose novel approaches to enhance situational awareness, its applications, and effective sharing of information among vehicles.;The communication technology for CAVs is known as vehicle-to-everything (V2x) communication, in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) have been targeted for the first round of deployment based on dedicated short-range communication (DSRC) devices for vehicles and road-side transportation infrastructures. Wireless communication among these entities creates self-organizing networks, known as Vehicular Ad-hoc Networks (VANETs). Due to the mobile, rapidly changing, and intrinsically error-prone nature of VANETs, traditional network architectures are generally unsatisfactory to address VANETs fundamental performance requirements. Therefore, we first investigate imperfections of the vehicular communication channel and propose a new modeling scheme for large-scale and small-scale components of the communication channel in dense vehicular networks. Subsequently, we introduce an innovative method for a joint modeling of the situational awareness and networking components of CAVs in a single framework. Based on these two models, we propose a novel network-aware broadcast protocol for fast broadcasting of information over multiple hops to extend the range of situational awareness. Afterward, motivated by the most common and injury-prone pedestrian crash scenarios, we extend our work by proposing an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection for vulnerable road users. Finally, as humans are the most spontaneous and influential entity for transportation systems, we design a learning-based driver behavior model and integrate it into our situational awareness component. Consequently, higher accuracy of situational awareness and overall system performance are achieved by exchange of more useful information

Visibility improvements through information provision regarding sun glare : a case study in Cape Town

Includes bibliographical references.Vision is inarguably a fundamental component of safe driving. Any obscurity in a driver’s vision can impose a threat to roadway safety due to interference with the driving task. Sun Glare is a hazard that few people anticipate. The sun is most potent to drivers when it is low in the sky on the horizon, particularly an hour or so after sunrise (dawn) and before sunset (dusk). The position of the sun and the angle of the rays during this period may render sun visors useless. This increases the risk of an accident as a result of interference with a driver’s ability to see the road ahead. The primary purpose of this study was to develop a method which will determine which areas in the City of Cape Town road network are exposed to direct sunlight, thus, making them vulnerable to road accident risk as a result of impaired vision. Additional objectives included the validation of the methodology by comparing field investigation outcomes with those of the established methodology. Considering the need for a tool with the ability to combine spatial data and sun position data, the ArcGIS software, which contains the hillshade tool, was selected for use in the study. The study was conducted for the City of Cape Town road network. The data used in the investigation included a Digital Elevation Model (DEM) and road network data of the study area. The DEM and road network data were both derived from a 10m topographical map. Sun position data (azimuth and altitude) was obtained from the Astronomical Applications Department of the U.S. Naval Observatory server. The sun position data obtained was for the morning and afternoon period of four days chosen for usage in the development of the model: Autumnal Equinox (AE), Spring Equinox (SE), Summer Solstice (SS) and Winter Solstice (WS). A 19º-25º altitude sun cone and a ±15º threshold (azimuth sun cone) were adopted for the filtering of road segment azimuth and slope. The field investigation carried out to validate the model was executed using a Road Eye JS-300 camera

Exploring the forecasting approach for road accidents: Analytical measures with hybrid machine learning

International audienceUrban traffic forecasting models generally follow either a Gaussian Mixture Model (GMM) or a Support Vector Classifier (SVC) to estimate the features of potential road accidents. Although SVC can provide good performances with less data than GMM, it incurs a higher computational cost. This paper proposes a novel framework that combines the descriptive strength of the Gaussian Mixture Model with the high-performance classification capabilities of the Support Vector Classifier. A new approach is presented that uses the mean vectors obtained from the GMM model as input to the SVC. Experimental results show that the approach compares very favorably with baseline statistical methods

Improving Pedestrian Safety with the Implementation of the Demand-Responsive Transverse Rumble Strip as a New Traffic Safety Countermeasure

The traditional TRS has been extensively used as a traffic calming device to provide cognitive alerts in the form of sound and vibration to drivers. However, TRS always remains fixed on the road and thus exerts cognitive alerts, irrespective of any potential downstream hazards. Moreover, the continuous exposure to rumble strips has been identified as a source of discomfort and annoyance for drivers, which limits its application to potentially useful scenarios. This study explores a rumble strip design with dynamic behavior named as Demand-Responsive Transverse Rumble Strip (DRTRS) in order to address the limitations of static TRS. The study incorporates DRTRS’ appropriate design dimensions and operation scheme, sound and vibration effect, speed-reducing effect, and pedestrian demand-based activation. In methodological procedures, the study explored four main aspects of DRTRS for designing and evaluating its effectiveness, which includes identification of optimum design dimensions, quantitative experimentation of in-vehicle sound and vibration, quantitative analysis of DRTRS effectiveness on drivers’ speed reductions, and prediction of the pedestrian demand for the activation mechanism of the DRTRS system. The study identified and selected the optimum width and depth of the rumble units of the DRTRS system prototype. The system was found to be effective in engaging the auditory and haptic senses of drivers, by generating discernible in-vehicle sound and vibration. Thereafter, the engagement of drivers’ cognitive senses yielded by the system had a significant effect on reducing vehicle speeds. In addition, the system can be set for flexible activation length based on the need from the crosswalks identified by pedestrian presence and prediction algorithms

Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]