A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding

Driver gaze plays an important role in different gaze-based applications such as driver attentiveness detection, visual distraction detection, gaze behavior understanding, and building driver assistance system. The main objective of this study is to perform a comprehensive summary of driver gaze fundamentals, methods to estimate driver gaze, and it's applications in real world driving scenarios. We first discuss the fundamentals related to driver gaze, involving head-mounted and remote setup based gaze estimation and the terminologies used for each of these data collection methods. Next, we list out the existing benchmark driver gaze datasets, highlighting the collection methodology and the equipment used for such data collection. This is followed by a discussion of the algorithms used for driver gaze estimation, which primarily involves traditional machine learning and deep learning based techniques. The estimated driver gaze is then used for understanding gaze behavior while maneuvering through intersections, on-ramps, off-ramps, lane changing, and determining the effect of roadside advertising structures. Finally, we have discussed the limitations in the existing literature, challenges, and the future scope in driver gaze estimation and gaze-based applications

Analysis Of Queue Characteristics At Signalized Intersections Near Highway-Railroad Grade Crossing

Analysis of traffic queues at signalized intersections which are in close proximity to highway- railroad grade crossings is of primary importance for determining if the normal signal operation needs to be preempted for railroad operations by providing a special signal mode for safe clearance of the queued vehicles from the tracks before the train arrival, and prohibiting any conflicting traffic movements towards the crossing. Such queuing analysis becomes even more critical where direct observations of traffic queues are not possible or where the assessment is needed for a future location. Inadequate estimation of queues from signalized intersections to the nearby railroad grade crossing can lead to severe safety issues. Underestimation of queue lengths may lead to an unsafe design while significantly overestimated queues may cause unnecessary traffic delays consequently leading to violations of the active traffic control devices at the crossing. In order to determine an adequate approach for reasonable estimation of queue lengths at signalized intersections near highway-railroad grade crossings, this dissertation first evaluated and compared different currently used microscopic simulation-based methods (i.e. Sim-Traffic and VISSIM) for their adequacy in estimating the queue lengths. After that several comparisons are made between the queue estimation from the simulation-based and other deterministic analytical methods including Highway Capacity Software, Synchro, and Railroad Assessment Tool. The comparisons drawn between each method helped identifying the differences and specific limitations of each method in including the impact of various important factors on the resulting queue estimation. The recommendations are provided on the basis of model capability to adequately count the impact of various significant traffic factors on queue estimation and considering minimizing the risk of underestimated queues. Based on the analysis findings, a microscopic simulation based procedure is developed using Sim-Traffic for estimating the 95th percentile queue lengths on various existing signalized intersection configurations near highway-rail grade crossings to help evaluate the need for signal preemption. In addition, recommendations are developed, if preemption is necessary, for determining queue clearance distance and minimum track clearance time. The recommended procedure is developed considering minimizing the risk of underestimated queues or unsafe design at such locations, and simplify the design and decision-making process

Safety problems in urban cycling mobility. A quantitative risk analysis at urban intersections

The attention to the most vulnerable road users has grown rapidly in recent decades. The experience gained reveals an important number of cyclist fatalities due to road crashes; most of which occur at intersections. In this study, dispersion of trajectories in urban intersections has been considered to identify the whole conflict area and the largest conflict areas between cars and bicycles, and the speeds have been used to calculate exposure time of cyclists and reaction time available to drivers to avoid collision. These data allow the summary approach to the problem, while a risk probability model has been developed to adopt an elementary approach analysis. A quantitative damage model has been proposed to classify each conflict point, and a probabilistic approach has been defined to consider the traffic volume and the elementary unit of exposure. The combination of damage and probability, permitted to assess the risk of crash, at the examined intersection. Three types of urban four-arm intersection, with and without bike paths, were considered. For each scheme, the authors assessed the risk of collision between the cyclist and the vehicle. The obtained results allowed the identification of the most hazardous maneuvers and highlighted that geometry and kinematics of traffic movements cannot be overlooked, when designing an urban road intersection. The strategy proposed by the authors could have a significant impact on the risk management of urban intersections. The obtained results and the proposed hazard estimation methodology could be used to design safer intersections

A Holistic Safety Benefit Assessment Framework for Heavy Goods Vehicles

In 2019, more than one million crashes occurred on European roads, resulting in almost 23,000 traffic fatalities. Although heavy goods vehicles (HGVs) were only involved in 4.4% of these crashes, their proportion in crashes with fatal outcomes was almost three times larger. This over-representation of HGVs in fatal crashes calls for actions that can support the efforts to realize the vision of zero traffic fatalities in the European Union. To achieve this vision, the development and implementation of passive as well as active safety systems are necessary. To prioritise the most effective systems, safety benefit estimations need to be performed throughout the development process. The overall aim of this thesis is to provide a safety benefit assessment framework, beyond the current state of the art, which supports a timely and detailed assessment of safety systems (i.e. estimation of the change in crash and/or injury outcomes in a geographical region), in particular active safety systems for HGVs. The proposed framework is based on the systematic integration of different data sources (e.g. virtual simulations and physical tests), using Bayesian statistical methods to assess the system performance in terms of the number of lives saved and injuries avoided. The first step towards the implementation of the framework for HGVs was an analysis of three levels of crash data that identified the most common crash scenarios involving HGVs. Three scenarios were recognized: HGV striking the rear-end of another vehicle, HGV turning right in conflict with a cyclist, and HGV in conflict with a pedestrian crossing the road. Understanding road user behaviour in these critical scenarios was identified as an essential element of an accurate safety benefit assessment, but sufficiently detailed descriptions of HGV driver behaviour are currently not available. To address this research gap, a test-track experiment was conducted to collect information on HGV driver behaviour in the identified cyclist and pedestrian target scenarios. From this information, HGV driver behaviour models were created. The results show that the presence of a cyclist or pedestrian creates different speed profiles (harder braking further away from the intersection) and changes in the gaze behaviours of the HGV drivers, compared to the same situation where the vulnerable road users are not present. However, the size of the collected sample was small, which posed an obstacle to the development of meaningful driver models. To overcome this obstacle, a framework to create synthetic populations through Bayesian functional data analysis was developed and implemented. The resulting holistic safety benefit assessment framework presented in this thesis can be used not only in future studies that assess the effectiveness of safety systems for HGVs, but also during the actual development process of advanced driver assistance systems. The research results have potential implications for policies and regulations (such as new UN regulations for mandatory equipment or Euro NCAP ratings) which are based on the assessment of the real-world benefit of new safety systems and can profit from the holistic safety benefit assessment framework