Ontology based Scene Creation for the Development of Automated Vehicles

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

Improving Drivers’ Behaviour When Partial Driving Automation Fails

With the advent of automated vehicle systems, the role of drivers has changed to a more supervisory role. However, it is known that all vehicles with Level 2 (L2) systems have a very specific operational design domain (ODD) and can only function on limited conditions. Hence, it is important for drivers to perceive the situations properly and regain the control from the L2 system when needed. As suggested by past research, designing an informative interface could help drivers in their new supervision and intervention role while driving with L2 vehicles by providing feedback to drivers when hazards or event that may cause system failure are detected. On the other hand there are many situations where these vehicles cannot detect hazards and provide any feedback prior to the event. In these cases, training programs which provide drivers with an experience of these system limitations and allow them to practice dealing with such limitations can prove to be effective countermeasures. The objective of the current study is to employ different methods (designing HMI and training drivers) to increase drivers’ situational awareness regarding operational design domain (ODD) and improve drivers performance in transfer of control situations while driving with level 2 (L2) automation features. This study includes two experiments- in first experiment, an informative dashboard interface was designed and tested through three phases (observation, prototyping, testing). Results from the testing phase showed that drivers who received the newly designed dashboards took back control more effectively and had more situational awareness compared to the control group. In the second experiment, a PC-based training program was designed and tested to improve drivers takeover response and situational awareness when L2 systems reach their ODD limits. Results showed drivers in the PC-based training group took back control more effectively when L2 systems reached their ODD limits and had more situational awareness compared to the drivers who received user manual or placebo training

ADVANCED VIRTUAL REALITY HEADSET BASED TRAINING TO IMPROVE YOUNG DRIVERS’ LATENT HAZARD ANTICIPATION ABILITY

Driving safety among young novice driver is one of the largest concern in the transportation domain. Many Paper-based or PC- based training program have been developed over the years to train the young novice driver to improve their driving skills (Hazard Anticipation). This training programs does help young novice driver to improve their situational awareness and so the hazard anticipation skills. But, there is one common problem with most of the currently available training programs. They are not very immersive, because such training program mostly provide plain view of the training scenario’s along with some description about the scenario and the subject trained in such training method needs to translate the provided knowledge in the plain view into the real-world driving. An Advanced training program on risk awareness and perception was developed and evaluated in Oculus rift platform. The primary objective is to train the young novice driver in the Virtual reality headset based risk awareness and perception training program and evaluate the trained driver in the driving simulator against the placebo trained young novice driver. The Virtual reality headset based risk awareness and perception training program (V-RAPT) is based on 3M Error-based Training approach where the driver will have 80 horizontal degrees’ and 90 vertical degrees’ field of view. Thirty-six drivers will receive training in the respective training methods- V-RAPT (Virtual reality headset based risk awareness and perception training), RAPT (PC- based risk awareness and perception training) and placebo training. Twelve young novice driver trained in the V-RAPT group will served as experimental group. Twenty-four other young novice will receive training in the RAPT and Placebo training respective will serve as control group. After training all three-group trained driver will be evaluated in the advanced driving simulator and the eye movement of the all thirty-six participants are recorded and measured. Vehicle measures such as acceleration, velocity and brake position is also recorded. The drivers’ score will based on whether or not their eye-fixations indicated recognition of potential risks in different high risk driving situations. The evaluation driver included six scenarios used in the V-RAPT training (near transfer scenarios) and four scenarios that were not used in the V-RAPT training (far transfer scenarios). Drivers who received the V-RAPT training are expected to drive more safely than the drivers who received either training. The V-RAPT trained drivers are expected to glance on regions (Hazard anticipation) where potential risks might appear than the drivers’ trained in the RAPT and Placebo training method. Further, The V-RAPT trained drivers are expected have slower average velocity and better brake position (Hazard mitigation) are compared to the driver trained in the other two training method

위험 상황 하 운전자 경험에 따른 스트레스 수준 및 주의 능력 차이 비교

학위논문 (석사) -- 서울대학교 대학원 : 인문대학 협동과정 인지과학전공, 2021. 2. 윤명환.The purpose of this study is to determine the differences in stress-level and attentional functions between experienced and non-experienced drivers during intersection-related hazard situations. A simulation experiment was conducted to twenty-one licensed drivers (15 males and 6 females, mean age 27.71 ± 3.62) which were categorized into two groups, experienced and novice, based on the frequency and length of their driving experience. The participants were asked to drive on the same lane at a constant speed for 9 randomized trials with three different conditions (no hazard, low hazard, high hazard). ECG, GSR and eye-tracking data were collected throughout the whole session and a subjective questionnaire measuring perceived stress and attention load was administered after every trial. Mixed-ANOVA showed significant variations in driving performance across conditions (p < 0.001 for both lane and speed deviation), but not in between groups. Similarly, GSR metrics (SCR, Sum of Amplitudes, Phasic Max) showed a progressive increase in stress from hazard conditions 1 to 3, but no differences were found between groups. In contrast, ECG measures (STD RR, RMSSD, HF) revealed that experienced drivers exhibited greater stress during intersection-related hazard situations than novices. In terms of attention, both AOI-based and non-AOI-based measures (fixation count, mean fixation duration, time-to-first-fixation and horizontal dispersion) demonstrate significant differences in attention functions across conditions, but group effects were only evident in time-to-first fixation metrics. It was revealed that experienced drivers were faster to attend to the hazard stimulus than novices. In addition, an interaction was also found between experience and condition in mean fixation duration. Experienced drivers showed proportional attention allocation to both the primary task and hazard stimulus during high hazard situations than novice drivers. The overall result of the psychophysiological measures was further affirmed by the results from the subjective questionnaire whereby experienced drivers exhibited more changes in stress-level and attention load as the condition changed. The results suggest that experienced drivers are more sensitive, in terms of stress and attention functions, to changes in driving conditions than novices. The results of this study may be applied in designing more effective training modules and driver support systems that would help drivers specifically during intersection-related hazard situations.본 연구의 목적은 교차로와 관련된 위험 상황 하에서 운전 경험의 수준이 다른 운전자 간 스트레스 수준과 주의 능력의 차이를 확인하는 것이다. 운전 빈도 및 기간에 따라 숙련된 운전자와 미숙한 운전자의 두 그룹으로 분류된 운전면허 자격을 보유한 21명의 운전자들 (남성 15명, 여성 6명, 평균 27.71 ± 3.62세)을 대상으로 차량 시뮬레이터를 활용한 실험이 수행되었다. 실험참여자들은 세 가지 위험 상황 조건(무위험, 저위험, 고위험)이 임의로 제시되는 환경에서 동일 차선을 유지하여 정속 주행하는 과업을 9회 반복 수행하였다. 과업이 수행되는 동안 ECG, GSR, 그리고 시선 추적 데이터가 수집되었고, 과업 수행 종료 후 주관적 설문지를 활용하여 인지된 스트레스 및 주의 부하가 측정되었다. Mixed-ANOVA 수행 결과, 운전 수행도는 차선 이탈 및 속도 이탈 측면에서 위험 상황 조건 별 유의한 차이가 있었으나(p < 0.001), 운전 경험 그룹 간의 차이는 없었다. 또한, GSR 척도(SCR, Sum of Amplitudes, Phasic Max)는 위험 상황 수준이 증가할수록 스트레스가 점진적으로 증가하는 것으로 확인할 수 있었으나, 운전 경험 그룹 간의 차이는 없었다. 반면, ECG 척도(STD RR, RMSSD, HF)는 숙련된 운전자 그룹이 미숙한 운전자 그룹보다 교차로 관련 위험 상황 동안 더 높은 스트레스를 나타낸다는 것을 보여주었다. 한편, 주의 능력 측면에서 AOI와 non-AOI 기반의 시선 추적 척도(fixation count, mean fixation duration, time-to-first-fixation and horizontal dispersion)는 위험 상황 조건 별 유의한 차이가 있었으나, 운전 경험 그룹 효과는 time-to-first-fixation에서만 나타났다. 이는 숙련된 운전자 그룹이 미숙한 운전자 그룹보다 위험 자극에 대해 상대적으로 신속하게 반응하기 때문인 것으로 판단된다. 게다가, mean fixation duration에서는 운전 경험과 위험 상황 조건의 교호작용 효과 또한 확인할 수 있었다. 숙련된 운전자 그룹은 미숙한 운전자 그룹보다 고위험 상황 하에서 주요 과업과 위험 자극 모두에 비례적으로 주의를 할당하는 것으로 나타났다. 숙련된 운전자 그룹이 미숙한 운전자 그룹보다 위험 상황 조건이 변함에 따라 스트레스 수준과 주의 부하의 변화가 더 심하다는 것을 나타내는 심리생리학적 척도들의 분석 결과는 주관적 설문지의 분석 결과를 통해 보다 확증되었다. 주관적 설문지의 결과 또한 숙련된 운전자 그룹이 미숙한 운전자 그룹보다 위험 상황 조건의 변화에 스트레스와 주의 능력 측면에서 보다 민감하다는 것을 보여주었다. 본 연구의 실험 결과는 숙련된 운전자와 미숙한 운전자의 두 그룹 간 뚜렷하면서 흥미로운 차이를 보여주었으며, 향후 교차로 관련 위험 상황 하에서 운전자의 안전에 도움을 줄 수 있는 지원 시스템과 효과적인 훈련 모듈을 설계하는 데 활용될 수 있을 것으로 기대된다.Abstract i Contents ii List of Tables iv List of Figures v Chapter 1 Introduction 1 1.1 Background 1 1.2 Research Objectives 4 1.3 Organization of the Thesis 4 Chapter 2 Literature Review 5 2.1 Overview 5 2.2 Driving Experience and Performance 5 2.3 Stress 8 2.3.1 Stress and Driving 9 2.3.2 Driving Experience and Stress Susceptibility 11 2.3.3 Psychophysiological Measures of Stress 12 2.3.4 Subjective Measures of Stress 14 2.4 Attentional Functions 15 2.4.1 Attention and Driving 16 2.4.2 Driving Experience and Attention 18 2.4.3 Eye-tracking Measures of Attention 20 2.4.4 Subjective Measures of Attention 21 Chapter 3 Driving Simulation Experiment 22 3.1 Overview and Hypotheses 22 3.2 Methods 23 3.2.1 Participants 23 3.2.2 Apparatus 24 3.2.3 Measures 26 3.2.4 Experimental Design 27 3.2.5 Experimental Procedure 29 3.2.6 Data Analysis 30 3.3 Results 31 Chapter 4 Discussion 58 4.1 Driving Performance 58 4.2 Stress Level 59 4.3 Attentional Functions 62 Chapter 5 Conclusion 65 5.1 Conclusion 65 5.2 Future Direction 65 Bibliography 66 국문초록 77Maste

Autonomous navigation in interaction-based environments - a case of non-signalised roundabouts

To reduce the number of collision fatalities at crossroads intersections many countries have started replacing intersections with non-signalised roundabouts, forcing the drivers to be more situationally aware and to adapt their behaviours according to the scenario. A non-signalised roundabout adds to the autonomous vehicle planning challenge, as navigating such interaction dependent scenarios safely, efficiently and comfortably has been a challenge even for human drivers. Unlike traffic signal controlled roundabouts where the merging order is centrally controlled, driving a non-signalised roundabout requires the individual actor to make the decision to merge based on the movement of other interacting actors. Most traditional autonomous planning approaches use rule-based speed assignment for generating admissible motion trajectories, which work successfully in non-interaction-based driving scenarios. They, however, are less effective in interaction-based scenarios as they lack the necessary ability to adapt the vehicle's motion according to the evolving driving scenario. In this paper, we demonstrate an Adaptive Tactical Behaviour Planner (ATBP) for an autonomous vehicle that is capable of planning human-like motion behaviours for navigating a non-signalised roundabout, combining naturalistic behaviour planning and tactical decision-making algorithm. The human driving simulator experiment used to learn the behaviour planning approach and ATBP design are described in the paper

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

The Impact of First-Person Perspective Text and Images on Drivers’ Comprehension, Learning Judgments, Attitudes, and Intentions Related to Safe Road-Sharing Behaviors

Drivers and cyclists lack an alignment of road sharing knowledge, attitudes, and expectations, resulting in unnecessary fatalities. Educational countermeasures need to present information that captures drivers’ interest by being personally relevant, facilitate elaboration and synthesis of new information with existing knowledge, and change attitudes, intentions, and behavior. Well-documented health-related communication methods were employed to determine their effectiveness in a transportation domain. Health countermeasure designers use first-person perspective to improve narrative instruction outcomes, based on the Elaboration Likelihood Model (ELM; Petty & Cacioppo, 1986). Exploring narrative perspective-taking as a design tool requires the integration of multiple disciplines. Our design case stems from the existing Virginia road-sharing safety educational handbook. The first study evaluated the effects of text-based information written from a first- and third-person perspective on cognitive and affective learning outcomes. The Theory of Planned Behavior framework (TPB; Ajzen, 1991) was used to interpret the following outcome measurements that are predictive of behavior: comprehension, judgments of learning, attitudes, and intentions. The second study employed the Cognitive Theory of Multimedia Learning (CTML; Mayer, 1997) to understand the interactions between text and visual perspectives on cognitive and affective learning outcomes. In addition, cognitive load, multiple knowledge types, and three behavioral intention components were also considered when evaluating the efficacy of first-person perspective. It was found that the first-person perspective effect used in the health domain does not transfer to a transportation domain. The data were explored further and discussed, as well as key limitations and possible future directions

Mobility and Aging: Older Drivers’ Visual Searching, Lane Keeping and Coordination

This thesis examined older drivers’ mobility and behaviour through comprehensive measurements of driver-vehicle-environment interaction and investigated the associations between driving behaviour and cognitive functions. Data were collected and analysed for 50 older drivers using eye tracking, GNSS tracking, and GIS. Results showed that poor selective attention, spatial ability and executive function in older drivers adversely affect lane keeping, visual search and coordination. Visual-motor coordination measure is sensitive and effective for driving assessment in older drivers