Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Overtaking a cyclist on a two-lane rural road with oncoming traffic is a challenging task for any driver. Failing this task can lead to severe injuries or even death, because of the potentially high impact speed in a possible collision. To avoid a rear-end collision with the cyclist, drivers need to make a timely and accurate decision about whether to steer and overtake the cyclist, or brake and let the oncoming traffic pass first. If this decision is delayed, for instance because the driver is distracted, neither braking nor steering may eventually keep the driver from crashing—at that point, rear-ending a cyclist may be the safest alternative for the driver. Active safety systems such as forward collision warning that help drivers being alert and avoiding collisions may be enhanced with driver models to reduce activations perceived as false positive. In this study, we developed a driver model based on logistic regression using data from a test-track experiment. The model can predict the probability and confidence of drivers braking and steering while approaching a cyclist during an overtaking, and therefore this model may improve collision warning systems. In both an in-sample and out-of-sample evaluation, the model identified drivers’ intent to overtake with high accuracy (0.99 and 0.90, respectively). The model can be integrated into a warning system that leverages the deviance of the actual driver behavior from the behavior predicted by the model to allow timely warnings without compromising driver acceptance

Extending teleoperated driving using a shared X-in-the-loop environment

The strong progress in modern vehicle system technology requires new methodological approaches for the development and validation of new vehicle systems. In particular, due to increasing automation, classical development methods and testing scenarios need to be evolved. Consequently, the publication focuses on an extension of teleoperated driving by the X-in-the-loop (XIL) approach. Within this framework, the classical concept based on VPN-LTE networking is analyzed and discussed at first. With this implementation, the remote control of a real vehicle is presented based on the use of a dynamic driving simulator. Especially for the development and validation of such concepts, an extension with the XIL methodology can improve this process. For this reason, the architecture of teleoperated driving is subsequently extended by networking with additional system components. The feasibility, the functionalities as well as the challenges that arise with such an extension based on the XIL methodology are shown.Within the scope of this study, the achieved transmission times for the control variables and for the video data stream are demonstrated. Based on different driving maneuvers, the achievable repeatability is discussed

AV의 Gap Settings를 고려한 고속도로 Lane-drop Bottleneck에서의 CAV 제어 전략 개발

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2023. 2. 이청원.Lane-drop bottleneck is a frequently observed bottleneck in a freeway due to lane closures, work zones, and incidents. A potential cause for a capacity drop at a lane-drop bottleneck is the critical conflicts by mandatory lane changing near the lane-dropping point, and the upstream inflow higher than the downstream capacity. Therefore, the throughput is expected to increase by operating CAV control strategy that can reduce disruption and keep the upstream inflow under downstream capacity. CAVs in this study are assumed to provide multiple gap settings, including the shortest and the longest gap settings currently available in commercial AVs. A novel concept that controls the gap setting of CAVs to increase throughput at a lane-drop bottleneck is proposed. The proposed strategy consists of merging control and inflow control. Merging control adjusts the gap setting of CAVs to a proposed gap setting that can reduce disruption caused by merging when applied to CAVs. Inflow control adjusts the gap setting of CAVs to either the shortest or the longest gap setting dynamically to regulate upstream inflow and keep bottleneck occupancy at the target occupancy. Proportional-Integral-Derivative (PID) controller was utilized for inflow control. To validate the proposed strategy, the simulation experiment was conducted with micro-simulation VISSIM. The results indicated that the proposed strategy prevents capacity drop and improves traffic flow efficiency in all demand scenarios under CAV environment. The proposed strategy also improved traffic flow efficiency under all simulated MPR scenarios, and the gain in performance was marginal for MPRs higher than 50%. Furthermore, the proposed strategy reduced CO2 emissions and the number of conflicts for all MPRs.차로감소 병목구간은 차로감소, 공사, 사고 등으로 인해 고속도로에서 자주 관측된다. 이러한 고속도로 차로감소 병목구간에서는 필수적인 차로변경으로 인한 차들 간의 상충, 그리고 상류부 유입교통량이 하류부 용량보다 큰 상황으로 인해 용량저하가 발생할 수 있다. 따라서 CAV 제어를 통해 차량 합류 행태를 개선하고 상류부 유입교통량을 조절할 수 있다면 정체구간 유출교통량을 늘릴 수 있을 것으로 기대된다. 본 연구는 CAV가 현재 판매되는 자율주행차들이 제공하는 차간거리설정을 포함하여 여러 차간거리설정들을 제공한다고 가정하였으며, CAV의 차간거리설정 제어를 통해 차로감소 병목구간의 유출교통량을 증가시킬 수 있는 새로운 개념의 전략을 제안하였다. 본 연구에서 제안하는 전략은 합류제어와 유입량제어로 구성된다. 합류제어는 CAV의 차간거리설정을 합류를 개선할 수 있도록 제안된 새로운 차간거리설정으로 조정한다. 유입량제어는 비례-적분-미분 제어기를 활용하여 CAV들의 차간거리설정을 가장 긴 설정 혹은 가장 짧은 설정으로 동적으로 제어함으로써 상류부 유입교통량을 조절하고 병목구간 점유율을 목표 점유율에 가깝게 유지하도록 한다. 본 연구는 제안된 전략의 성능을 평가하기 위해 미시교통류시뮬레이션 VISSIM에 전략을 구현하고 시뮬레이션을 진행하였다. 시뮬레이션 결과, CAV 환경에서 본 전략은 모든 용량 시나리오에 대해 용량저하를 방지하고 운영성을 개선하였다. 또한 본 전략은 검토된 모든 CAV 시장점유율에서 운영성을 개선하였고, 시장점유율 50% 이상에서는 개선 정도의 증가율이 미미함을 확인하였다. 운영성 뿐만 아니라 환경성과 안전성 측면에서도 모든 시장점유율에서 전략이 효과적임을 확인하였다.Chapter 1. Introduction 1 Chapter 2. Literature Review 4 2.1. Microscopic Control Algorithms 4 2.2. Macroscopic Control Algorithms 6 2.3. Impact of AV on Traffic Flow 7 Chapter 3. Methodology 9 3.1. Vehicle Modeling 9 3.2. Gap Setting Control Strategy 13 Chapter 4. Simulation Analysis 20 4.1. Simulation Design 20 4.2. Results and Discussions 27 Chapter 5. Conclusions 38 Bibliography 40 Abstract in Korean 46석

How can on-road hazard perception and anticipation be improved? Evidence from the body

The present research is aimed at investigating processes associated with learning how to drive safely. We were particularly interested in implicit mechanisms related to the automatic processing system involved in decision making in risky situations (Slovic et al., 2007). The operation of this system is directly linked to experiential and emotional reactions and can be monitored by measuring psychophysiological variables, such as skin conductance responses (SCRs). We focused specifically on the generalization of previously acquired skills to new and never before encountered road scenarios. To that end, we compared the SCRs of two groups of participants engaged, respectively, in two distinctive modes of moped-riding training. The active group proceeded actively, via moped, through several simulated courses, whereas the passive group watched video of the courses performed by the former group and identified hazards. Results indicate that the active group not only demonstrated improved performance in the second session, which involved the same simulated courses, but also showed generalization to new scenes in the third session. Moreover, SCRs to risky scenes, although present in both groups, were detectable in a higher proportion in the active group, paralleling the degree of risk confronted as the training progressed. Finally, the anticipatory ability demonstrated previously (and replicated in the present study), which was evident in the repeated performance of a given scenario, did not seem to generalize to the new scenarios confronted in the last session

Methods and techniques for analyzing human factors facets on drivers

Mención Internacional en el título de doctorWith millions of cars moving daily, driving is the most performed activity worldwide. Unfortunately, according to the World Health Organization (WHO), every year, around 1.35 million people worldwide die from road traffic accidents and, in addition, between 20 and 50 million people are injured, placing road traffic accidents as the second leading cause of death among people between the ages of 5 and 29. According to WHO, human errors, such as speeding, driving under the influence of drugs, fatigue, or distractions at the wheel, are the underlying cause of most road accidents. Global reports on road safety such as "Road safety in the European Union. Trends, statistics, and main challenges" prepared by the European Commission in 2018 presented a statistical analysis that related road accident mortality rates and periods segmented by hours and days of the week. This report revealed that the highest incidence of mortality occurs regularly in the afternoons during working days, coinciding with the period when the volume of traffic increases and when any human error is much more likely to cause a traffic accident. Accordingly, mitigating human errors in driving is a challenge, and there is currently a growing trend in the proposal for technological solutions intended to integrate driver information into advanced driving systems to improve driver performance and ergonomics. The study of human factors in the field of driving is a multidisciplinary field in which several areas of knowledge converge, among which stand out psychology, physiology, instrumentation, signal treatment, machine learning, the integration of information and communication technologies (ICTs), and the design of human-machine communication interfaces. The main objective of this thesis is to exploit knowledge related to the different facets of human factors in the field of driving. Specific objectives include identifying tasks related to driving, the detection of unfavorable cognitive states in the driver, such as stress, and, transversely, the proposal for an architecture for the integration and coordination of driver monitoring systems with other active safety systems. It should be noted that the specific objectives address the critical aspects in each of the issues to be addressed. Identifying driving-related tasks is one of the primary aspects of the conceptual framework of driver modeling. Identifying maneuvers that a driver performs requires training beforehand a model with examples of each maneuver to be identified. To this end, a methodology was established to form a data set in which a relationship is established between the handling of the driving controls (steering wheel, pedals, gear lever, and turn indicators) and a series of adequately identified maneuvers. This methodology consisted of designing different driving scenarios in a realistic driving simulator for each type of maneuver, including stop, overtaking, turns, and specific maneuvers such as U-turn and three-point turn. From the perspective of detecting unfavorable cognitive states in the driver, stress can damage cognitive faculties, causing failures in the decision-making process. Physiological signals such as measurements derived from the heart rhythm or the change of electrical properties of the skin are reliable indicators when assessing whether a person is going through an episode of acute stress. However, the detection of stress patterns is still an open problem. Despite advances in sensor design for the non-invasive collection of physiological signals, certain factors prevent reaching models capable of detecting stress patterns in any subject. This thesis addresses two aspects of stress detection: the collection of physiological values during stress elicitation through laboratory techniques such as the Stroop effect and driving tests; and the detection of stress by designing a process flow based on unsupervised learning techniques, delving into the problems associated with the variability of intra- and inter-individual physiological measures that prevent the achievement of generalist models. Finally, in addition to developing models that address the different aspects of monitoring, the orchestration of monitoring systems and active safety systems is a transversal and essential aspect in improving safety, ergonomics, and driving experience. Both from the perspective of integration into test platforms and integration into final systems, the problem of deploying multiple active safety systems lies in the adoption of monolithic models where the system-specific functionality is run in isolation, without considering aspects such as cooperation and interoperability with other safety systems. This thesis addresses the problem of the development of more complex systems where monitoring systems condition the operability of multiple active safety systems. To this end, a mediation architecture is proposed to coordinate the reception and delivery of data flows generated by the various systems involved, including external sensors (lasers, external cameras), cabin sensors (cameras, smartwatches), detection models, deliberative models, delivery systems and machine-human communication interfaces. Ontology-based data modeling plays a crucial role in structuring all this information and consolidating the semantic representation of the driving scene, thus allowing the development of models based on data fusion.I would like to thank the Ministry of Economy and Competitiveness for granting me the predoctoral fellowship BES-2016-078143 corresponding to the project TRA2015-63708-R, which provided me the opportunity of conducting all my Ph. D activities, including completing an international internship.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Armingol Moreno.- Secretario: Felipe Jiménez Alonso.- Vocal: Luis Mart

Vehicle Tracking and Motion Estimation Based on Stereo Vision Sequences

In this dissertation, a novel approach for estimating trajectories of road vehicles such as cars, vans, or motorbikes, based on stereo image sequences is presented. Moving objects are detected and reliably tracked in real-time from within a moving car. The resulting information on the pose and motion state of other moving objects with respect to the own vehicle is an essential basis for future driver assistance and safety systems, e.g., for collision prediction. The focus of this contribution is on oncoming traffic, while most existing work in the literature addresses tracking the lead vehicle. The overall approach is generic and scalable to a variety of traffic scenes including inner city, country road, and highway scenarios. A considerable part of this thesis addresses oncoming traffic at urban intersections. The parameters to be estimated include the 3D position and orientation of an object relative to the ego-vehicle, as well as the object's shape, dimension, velocity, acceleration and the rotational velocity (yaw rate). The key idea is to derive these parameters from a set of tracked 3D points on the object's surface, which are registered to a time-consistent object coordinate system, by means of an extended Kalman filter. Combining the rigid 3D point cloud model with the dynamic model of a vehicle is one main contribution of this thesis. Vehicle tracking at intersections requires covering a wide range of different object dynamics, since vehicles can turn quickly. Three different approaches for tracking objects during highly dynamic turn maneuvers up to extreme maneuvers such as skidding are presented and compared. These approaches allow for an online adaptation of the filter parameter values, overcoming manual parameter tuning depending on the dynamics of the tracked object in the scene. This is the second main contribution. Further issues include the introduction of two initialization methods, a robust outlier handling, a probabilistic approach for assigning new points to a tracked object, as well as mid-level fusion of the vision-based approach with a radar sensor. The overall system is systematically evaluated both on simulated and real-world data. The experimental results show the proposed system is able to accurately estimate the object pose and motion parameters in a variety of challenging situations, including night scenes, quick turn maneuvers, and partial occlusions. The limits of the system are also carefully investigated.In dieser Dissertation wird ein Ansatz zur Trajektorienschätzung von Straßenfahrzeugen (PKW, Lieferwagen, Motorräder,...) anhand von Stereo-Bildfolgen vorgestellt. Bewegte Objekte werden in Echtzeit aus einem fahrenden Auto heraus automatisch detektiert, vermessen und deren Bewegungszustand relativ zum eigenen Fahrzeug zuverlässig bestimmt. Die gewonnenen Informationen liefern einen entscheidenden Grundstein für zukünftige Fahrerassistenz- und Sicherheitssysteme im Automobilbereich, beispielsweise zur Kollisionsprädiktion. Während der Großteil der existierenden Literatur das Detektieren und Verfolgen vorausfahrender Fahrzeuge in Autobahnszenarien adressiert, setzt diese Arbeit einen Schwerpunkt auf den Gegenverkehr, speziell an städtischen Kreuzungen. Der Ansatz ist jedoch grundsätzlich generisch und skalierbar für eine Vielzahl an Verkehrssituationen (Innenstadt, Landstraße, Autobahn). Die zu schätzenden Parameter beinhalten die räumliche Lage des anderen Fahrzeugs relativ zum eigenen Fahrzeug, die Objekt-Geschwindigkeit und -Längsbeschleunigung, sowie die Rotationsgeschwindigkeit (Gierrate) des beobachteten Objektes. Zusätzlich werden die Objektabmaße sowie die Objektform rekonstruiert. Die Grundidee ist es, diese Parameter anhand der Transformation von beobachteten 3D Punkten, welche eine ortsfeste Position auf der Objektoberfläche besitzen, mittels eines rekursiven Schätzers (Kalman Filter) zu bestimmen. Ein wesentlicher Beitrag dieser Arbeit liegt in der Kombination des Starrkörpermodells der Punktewolke mit einem Fahrzeugbewegungsmodell. An Kreuzungen können sehr unterschiedliche Dynamiken auftreten, von einer Geradeausfahrt mit konstanter Geschwindigkeit bis hin zum raschen Abbiegen. Um eine manuelle Parameteradaption abhängig von der jeweiligen Szene zu vermeiden, werden drei verschiedene Ansätze zur automatisierten Anpassung der Filterparameter an die vorliegende Situation vorgestellt und verglichen. Dies stellt den zweiten Hauptbeitrag der Arbeit dar. Weitere wichtige Beiträge sind zwei alternative Initialisierungsmethoden, eine robuste Ausreißerbehandlung, ein probabilistischer Ansatz zur Zuordnung neuer Objektpunkte, sowie die Fusion des bildbasierten Verfahrens mit einem Radar-Sensor. Das Gesamtsystem wird im Rahmen dieser Arbeit systematisch anhand von simulierten und realen Straßenverkehrsszenen evaluiert. Die Ergebnisse zeigen, dass das vorgestellte Verfahren in der Lage ist, die unbekannten Objektparameter auch unter schwierigen Umgebungsbedingungen, beispielsweise bei Nacht, schnellen Abbiegemanövern oder unter Teilverdeckungen, sehr präzise zu schätzen. Die Grenzen des Systems werden ebenfalls sorgfältig untersucht

Factors that influence visual attention and their effects on safety in driving: an eye movement tracking approach

Statistics show that a high percentage of road related accidents are due to factors that cause impaired driving. Since information extraction in driving is predominantly a visual task, visual distraction and its implications are therefore important safety issues. The main objective of this research is to study some of the implications of demands to human’s attention and perception and how it affects performance of tasks such as driving. Specifically, the study aims to determine the changes that occur in the visual behavior of drivers with different levels of driving experience by tracking the movement of the eye; examine the effects of different levels of task complexity on visual fixation strategies and visual stimulus recognition; investigate the effects of secondary task on attentional and visual focus and its impact on driving performance; and evaluate the implications of the use of information technology device (cellular phone) while driving on road safety. Thirty-eight students participated in the study consisting of two experiments. In the first experiment, the participants performed two driving sessions while wearing a head mounted eye tracking device. The second experiment involved driving while engaging in a cellular phone conversation. Fixation location, frequency, duration and saccadic path, were used to analyze eye movements. The study shows that differences in visual behavior of drivers exist; wherein drivers with infrequent driving per week fixated more on the dashboard area than on the front view (F(3,26) = 3.53, p\u3c0.05), in contrast to the driver with more frequent use of vehicle per week where higher fixations were recorded in the front/center view (F(3,26) = 4.26). The degree of visual distraction contributes to the deterioration of driving resulting to 55% more driving errors committed. Higher time where no fixation was detected was observed when driving with distraction (from 96% to 91% for drivers with less frequency of vehicle use and 55% to 44% for drivers with more frequent use of vehicle). The number of pre-identified errors committed increased from 64 to 81, due to the effect of visual tunneling. This research presents objective data that strengthens the argument on the detrimental effects of distraction in driving

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor