Investigation of traffic conflicts at signalised intersections in Warsaw

Although traffic safety situation in general is improving, the numbers of pedestrians and cyclists hit when crossing a road have not significantly decreased recently. Based on police accident records for years 2010-2014, some 735 pedestrians and 505 cyclists were hit by motor vehicles in Warsaw. Investigation reported in this paper is a part of the European project InDeV. One aim of the project is to find correlation between accidents and traffic conflicts and thus provide a solid base for using surrogate safety measures as safety diagnostic tools. Three typical signalised intersections in Warsaw were selected for video recording. Relevant encounters between motor vehicles and vulnerable road users (pedestrians and cyclists) were identified and analysed using programs RUBA and T-Analyst. The paper describes the semiautomatic video data processing and problems regarding some technical and methodological aspects of conflict detection. Based on video analysis of 24 hours of recording for each intersection, preliminary characteristics of encounters between pedestrians/cyclists and motorised vehicles have been developed. Statistical distributions of encounter parameters such as time-to-collision (TTC) and post-encroachment time (PET) are presented. These will be used in the development of appropriate safety indicators

Advanced traffic video analytics for robust traffic accident detection

Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time. First, a new foreground detection method is applied in order to detect the moving vehicles and subtract the ever-changing background in the traffic video frames captured by static or non-stationary cameras. For the traffic videos captured during day-time, the cast shadows degrade the performance of the foreground detection and road segmentation. A novel cast shadow detection method is therefore presented to detect and remove the shadows cast by moving vehicles and also the shadows cast by static objects on the road. Second, a new method is presented to detect the region of interest (ROI), which applies the location of the moving vehicles and the initial road samples and extracts the discriminating features to segment the road region. After detecting the ROI, the moving direction of the traffic is estimated based on the rationale that the crashed vehicles often make rapid change of direction. Lastly, single-vehicle traffic accidents and trajectory conflicts are detected using the first-order logic decision-making system. The experimental results using publicly available videos and a dataset provided by the New Jersey Department of Transportation (NJDOT) demonstrate the feasibility of the proposed methods. Additionally, the main challenges and future directions are discussed regarding (i) improving the performance of the foreground segmentation, (ii) reducing the computational complexity, and (iii) detecting other types of traffic accidents

Simulations for Training Machine Learning Models for Autonomous Vehicles

Masinõppe mudelite treenimine autonoomsete sõidukite jaoks nõuab palju andmeid, mille käsitsi märgendamine on aeganõudev. Simulatsioonid aitavad seda protsessi automatiseerida. Käesolev töö koostab ülevaate 12-st internetiotsingu abil leitud simulatsioonist ja analüüsib neid lähtuvalt nende sobivusest maastikul liikuvatele sõidukitele (säilitades võimaluse liikuda ka linnakeskkonnas).Training machine learning models for autonomous vehicles requires a lot of data which is time consuming and tedious to label manually. Simulated virtual environments help to automate this process. In this work these virtual environments are called simulations. The goal of this thesis is to survey the most suitable simulations for off-road vehicles (while not discarding the urban option). Only the simulations which provide labeled output data, are included in this work. The chosen 12 simulations are surveyed based on the information found online. The simulations are then analyzed based on the predefined features and categorized according to their suitability for training machine learning models for off-road vehicles. The results are shown in a table for comparison. The main purpose of this work is to map the seemingly large landscape of simulations and give a compact picture of the situation

Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior

Abstract—Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, inter- active motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part II of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychological models, from the perspective of an AV designer. This self-contained Part II covers the higher levels of this stack, consisting of models of pedestrian behaviour, from prediction of individual pedestrians’ likely destinations and paths, to game-theoretic models of interactions between pedestrians and autonomous vehicles. This survey clearly shows that, although there are good models for optimal walking behaviour, high-level psychological and social modelling of pedestrian behaviour still remains an open research question that requires many conceptual issues to be clarified. Early work has been done on descriptive and qualitative models of behaviour, but much work is still needed to translate them into quantitative algorithms for practical AV control

Surrogate safety measures and traffic conflict observations.

The chapter primarily focuses on observing traffic conflicts (also known as near-accidents) as a site-based road safety analysis technique. Traffic conflicts are a type of surrogate safety measure. The term surrogate indicates that non-accident-based indicators are used to assess VRU safety instead ofthe more traditional approach focusing on accidents (see chapter 2). The theory underpinning surrogate safety measures is briefly described, followed by a discussion on the characteristics of the traffic conflict technique. Next, guidelines for conducting traffic conflict observations using trained human observers or video cameras are presented. Chapter 4 concludes with examples of the use of the traffic conflict technique in road safety studies focusing on VRUs