The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios

In the development of highly automated driving systems (L3 and 4), much research has been done on the subject of driver takeover. Strong focus has been placed on the takeover quality. Previous research has shown that one of the main influencing factors is the complexity of a traffic situation that has not been sufficiently addressed so far, as different approaches towards complexity exist. This paper differentiates between the objective complexity and the subjectively perceived complexity. In addition, the familiarity with a takeover situation is examined. Gold et al. show that repetition of takeover scenarios strongly influences the take-over performance. Yet, both complexity and familiarity have not been considered at the same time. Therefore, the aim of the present study is to examine the impact of objective complexity and familiarity on the subjectively perceived complexity and the resulting takeover quality. In a driving simulator study, participants are requested to take over vehicle control in an uncritical situation. Familiarity and objective complexity are varied by the number of surrounding vehicles and scenario repetitions. Subjective complexity is measured using the NASA-TLX; the takeover quality is gathered using the take-over controllability rating (TOC-Rating). The statistical evaluation results show that the parameters significantly influence the takeover quality. This is an important finding for the design of cognitive assistance systems for future highly automated and intelligent vehicles

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.University of Michigan McityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162593/1/AAP_physiological_responses_HF_template.pdfSEL

Prediction of drivers’ performance in highly automated vehicles

Purpose: The aim of this research was to assess the predictability of driver’s response to critical hazards during the transition from automated to manual driving in highly automated vehicles using their physiological data.Method: A driving simulator experiment was conducted to collect drivers’ physiological data before, during and after the transition from automated to manual driving. A total of 33 participants between 20 and 30 years old were recruited. Participants went through a driving scenario under the influence of different non-driving related tasks. The repeated measures approach was used to assess the effect of repeatability on the driver’s physiological data. Statistical and machine learning methods were used to assess the predictability of drivers’ response quality based on their physiological data collected before responding to a critical hazard. Findings: - The results showed that the observed physiological data that was gathered before the transition formed strong indicators of the drivers’ ability to respond successfully to a potential hazard after the transition. In addition, physiological behaviour was influenced by driver’s secondary tasks engagement and correlated with the driver’s subjective measures to the difficulty of the task. The study proposes new quality measures to assess the driver’s response to critical hazards in highly automated driving. Machine learning results showed that response time is predictable using regression methods. In addition, the classification methods were able to classify drivers into low, medium and high-risk groups based on their quality measures values. Research Implications: Proposed models help increase the safety of automated driving systems by providing insights into the drivers’ ability to respond to future critical hazards. More research is required to find the influence of age, drivers’ experience of the automated vehicles and traffic density on the stability of the proposed models. Originality: The main contribution to knowledge of this study is the feasibility of predicting drivers’ ability to respond to critical hazards using the physiological behavioural data collected before the transition from automated to manual driving. With the findings, automation systems could change the transition time based on the driver’s physiological state to allow for the safest transition possible. In addition, it provides an insight into driver’s readiness and therefore, allows the automated system to adopt the correct driving strategy and plan to enhance drivers experience and make the transition phase safer for everyone.</div

Automated driving: A literature review of the take over request in conditional automation

This article belongs to the Special Issue Autonomous Vehicles TechnologyIn conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver's state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions.This work was funded by the Austrian Ministry for Climate Action, Environment, Energy, Mobility, Innovation, and Technology (BMK) Endowed Professorship for Sustainable Transport Logistics 4.0; the Spanish Ministry of Economy, Industry and Competitiveness under the TRA201563708-R and TRA2016-78886-C3-1-R project; open access funding by the Johannes Kepler University Linz

Impact of Smart Phones’ Interaction Modality on Driving Performance for Conventional and Autonomous Vehicles

Distracted driving related to cell phone usage ranks among the top three causes of fatal crashes on the road. Although forty-eight of 50 U.S. states allow the use of personal devices if operated hands-free and secured in the vehicle, scientific studies have yet to quantify the safety improvement presumed to be introduced by voice-to-text interactions. Thus, this study investigated how different modes of interaction of drivers with a smart phone (i.e., manual texting vs. vocal input) affect drivers’ distraction and performance in both conventional and semi-autonomous vehicles. The study was executed in a full-car integrated simulator and tested a population of 32 drivers. The study considered two scenarios: (1) conventional manual driving in a suburban environment with intersection stops; and (2) control takeover from an engaged autonomous vehicle that reverted to manual driving at a highway exit. The quality of execution of maneuvers as well as timing and tracking of eye-gaze focus areas were assessed in both scenarios. Results demonstrated that while participants perceived an increased level of safety while using the hands-free interface, response times and drift did not significantly differ from those manually texting. Furthermore, even though participants perceived a greater effort in accomplishing the text reply through the manual interface, none of the measured quantities for driving performance or eye-gaze focus revealed statistical difference between the two interfaces, ultimately calling into question the assumption of greater safety implicit in the laws allowing hands-free devices

A psychophysiological insight into driver state during highly automated driving

The aim of this research was to investigate and validate the usage of physiological measures as an objective indicator of driver state in dynamic driving environments, and understand if such a methodology can be used to measure driver discomfort, and high workload. The work addressed questions relating to: (i) detecting and removing motion artefacts from electrodermal activity (EDA) signals in dynamic driving environments; (ii) primary factors contributing to driver discomfort during automation, measured in terms of their physiological state; (iii) understanding changes in drivers’ workload levels at different stages of automation, as indicated by electrocardiogram (ECG) and EDA-based measures and; (iv) how drivers’ attentional demands and workload levels are affected at different stages of automation, measured using eye tracking-based metrics. A series of experiments were developed to manipulate drivers’ discomfort and workload levels. The analysis around driver discomfort focused on automated driving, whereas drivers’ workload levels were investigated during automation, and during resumption of control from automation, in a series of car-following scenarios. Our results indicated that phasic EDA was able to pick up discomfort experienced by the driver during automation, and correlated to drivers’ subjective ratings of discomfort. Narrower roads, higher resultant acceleration forces and how the automated vehicle negotiated different road geometries all influenced driver discomfort. We observed that drivers’ workload levels were captured by ECG and EDA-based signals, with phasic component of EDA signal being more sensitive to short term variations in driver workload. Similar results were observed in drivers’ pupil diameter values, as well as subjective ratings of workload. Factors such as engagement in a non-driving related task (NDRT), presence of a lead vehicle while maintaining a short time headway, and takeovers, all seemed to increase drivers’ workload levels. Future work can build on this research by incorporating sensor fusion of ECG and EDA-based data, along with eye tracking, to help improve the accuracy and capabilities of future driver state monitoring systems

Drivers\u27 Ability to Engage in a Non-Driving Related Task While in Automated Driving Mode in Real Traffic

Engaging in non-driving related tasks (NDRTs) while driving can be considered distracting and safety detrimental. However, with the introduction of highly automated driving systems that relieve drivers from driving, more NDRTs will be feasible. In fact, many car manufacturers emphasize that one of the main advantages with automated cars is that it "frees up time" for other activities while on the move. This paper investigates how well drivers are able to engage in an NDRT while in automated driving mode (i.e., SAE Level 4) in real traffic, via a Wizard of Oz platform. The NDRT was designed to be visually and cognitively demanding and require manual interaction. The results show that the drivers\u27 attention to a great extent shifted from the road ahead towards the NDRT. Participants could perform the NDRT equally well as when in an office (e.g. correct answers, time to completion), showing that the performance did not deteriorate when in the automated vehicle. Yet, many participants indicated that they noted and reacted to environmental changes and sudden changes in vehicle motion. Participants were also surprised by their own ability to, with ease, disconnect from driving. The presented study extends previous research by identifying that drivers to a high extent are able to engage in a NDRT while in automated mode in real traffic. This is promising for future of automated cars ability to "free up time" and enable drivers to engage in non-driving related activities