Predicting Sleepiness from Driving Behaviour

This research investigates the use of objective EEG analysis to determine multiple levels of sleepiness in drivers. In the literature, current methods propose a binary (awake or sleep) or ternary (awake, drowsy or sleep) classification of sleepiness. Having few classification of sleepiness increases the risk of the driver reaching dangerous levels of sleepiness before a safety system can prevent it. Also, these methods are based on subjective analysis of physiological variables, which leads to lack of reproducibility and loss of data, when a lack of consensus is reached amongst the EEG experts. Therefore, the doctoral challenge was to determine whether multiple levels of sleepiness could be defined with high accuracy, using an objective analysis of EEG, a reliable indicator of sleepiness. The study identified awake, post-awake, pre-sleep and sleep as the multiple levels of sleepiness through the objective analysis of EEG. The research used Neural Networks, a type of Machine Learning algorithm, to determine the accuracy of the proposed multiple levels of sleepiness. The Neural Networks were trained using driving and physiological behaviour. The EEG data and the driving and physiological variables were obtained through a series of experiments aimed to induce sleepiness, conducted in the driving simulator at the University of Leeds. As the Neural Network obtained high accuracy when differentiating between awake and sleep and between post-awake and pre-sleep, it led to the conclusion that the proposed objective classification based on objective EEG analysis was suitable. However, this study did not reach the highest levels of accuracy when the 4 levels of sleepiness are combined, nevertheless the solutions proposed by the researcher to be carried in future work can contribute towards increasing the accuracy of the proposed method

Modelling discomfort: How do drivers feel when cyclists cross their path?

Introduction: Even as worldwide interest in bicycling continues to grow, cyclists constitute a large part of road fatalities. A major part of the fatalities occurs when cyclists cross a vehicle path. Active safety systems and automated driving systems may already account for these interactions in their control algorithms. However, the driver behaviour models that these systems use may not be optimal in terms of driver acceptance. If the systems could estimate driver discomfort, their acceptance might be improved.Method: This study investigated the degree of discomfort experienced by drivers when cyclists crossed their travel path. Participants were instructed to drive through an intersection in a fixed-base simulator or on a test track, following the same experimental protocol. The effects of demographic variables (age, gender, driving frequency, and yearly mileage), controlled variables (car speed, bicycle speed, and bicycle-car configuration), and a visual cue (car’s time-to-arrival at the intersection when the bicycle appears; TTAvis) on self-reported discomfort were analysed using cumulative link mixed models (CLMM).Results: Results showed that demographic variables had a significant effect on the discomfort felt by drivers—and could explain the variability observed between drivers. Across both experimental environments, the controlled variables were shown to significantly influence discomfort. TTAvis was shown to have a significant effect on discomfort as well; the closer to zero TTAvis was (i.e., the more critical the situation), the more likely the driver red great discomfort. The prediction accuracies of the CLMM with controlled variables and the CLMM with the visual cue were similar, with an average accuracy between 40 and 50%. Surprise trials in the simulator experiment, in which the bicycle appeared unexpectedly, improved the prediction accuracy of the models, more notably the CLMM including TTAvis. Conclusions: The results suggest that the discomfort was mainly driven by the visual cue rather than the deceleration cues. Thus, it is suggested that an algorithm that estimates driver discomfort be included in active safety systems and autonomous driving systems. The CLMM including TTAvis was presented as a potential candidate to serve this purpose

Deliverable 7.2. Report on methodology for balancing user acceptance, robustness and performance

The primary goal of this deliverable is to provide an overview of the methodology for acceptance testing that will be used during the tests conducted in T7.1, T7.2 and T7.3 within the PROSPECT project. The report starts with a description of the main characteristics of the most relevant accident scenarios where safety improvements are necessary. Among all use cases identified in WP3, twelve have been especially selected by the project to be implemented in the demonstrators: 9 for cyclists and 3 for pedestrians. Behaviours such as the velocity, distance and offset of the vehicle and cyclist are defined, so that the Safe Scenario, Critical Scenario and Possible Critical Scenario can be realized on the test tracks or in simulator environments. A literature review covering acceptance evaluation issues is then presented, outlining the questionnaires that are generally used to evaluate subjective measures, such as acceptance and trust. The methodology developed for Task 7.3 is then based on such questionnaires to be administered in tests and experiments that will evaluate PROSPECT systems. By using common questionnaires, this task facilitates an overall evaluation of the acceptance of all the developed functions. The methodology is presented in section 4 of this report, including a tool for data collection (LimeSurvey). This tool makes it possible for participants in evaluation studies to answer questions on various displays, to the convenience of the experimenters. In order to balance the user acceptance to the robustness and performance of the tested systems, all answers to the questionnaires will be linked to the PROSPECT functions tested and to the quality of the PROSPECT systems functioning. This methodology will be used at different times of the tests: before running a test/experiment (questionnaire 1 - participant information and questionnaire 3 - global expected acceptance of the system or a priori acceptability), during the test/experiment (questionnaire 2 - feedback on each situation) and after the test/experiment (questionnaire 3 - global acceptance of the system after having experienced it). At the end of this document, a section briefly describes all the experiments currently planned that will use the methodology within WP7. Their results will be reported in Deliverable 7.3 Report on simulator test results and driver acceptance of PROSPECT functions

D7.3 Report on simulator test results and driver acceptance of PROSPECT functions

The process of developing new automotive systems includes various testing cycles to assure a save operation in traffic. Physical system testing on test tracks is very important for this purpose, but rather expensive and might only become possible in later stages of the development process. Using a virtual simulation environment offers a safe possibility of testing new systems in early stages of development. Aditionally, driver-in-the-loop tests at test track and in a virtual simulator make it possible to evaluate driver reaction and potential acceptance by the future users of those systems. Within PROSPECT the new functions are investigated under various aspects in several simulator studies and test track studies. This deliverable D7.3 gives detailed information of conduction and results of the each study. Three studies focus exclusively on the for Vulnerable Road Users (VRUs) specifically dangerous urban intersection scenarios. The first of those studies examines the driver behaviour in a turning situation when a byciclist might be crossing. The described phenomena are looked-but-failed-to-see and failed-to-look. The second study, which provides an initial step in this line of research, analyzed the acceptance of issued forward collision warning times. The positioning of the potential accident opponent and the subjective feeling towards the criticality of the situation by the driver were key parameters taken into account. Last, but not least the acceptance of an intersection assist autonomous emergency braking systems was tested regarding the acceptance of potential buyers. The study was run for five days in a row for each participant to be able to judge the behaviour in a comuting situation. Two studies focused on longitudinal scenarios. Both studies followed the same design, but one was conducted on a test track and the other one in a simulator. The main objective was to investigate drivers reactions to FCW warnings and Active Steering interventions in critical VRU scenarios in case of a distraction of the driver. Additionally, the test track study was used to validate the results from the simulator study. The results of those studies are the basis for a wide acceptance evaluation of the systems. No system is an asset in increasing road safety if it is not accepted by the user and therefore turned off, if it is not required the system to be default on in consumer tests. Complemented by an additional acceptance study where the participants had to give their opinion of those systems after they watched videos of dangerous situations, the acceptance was analyzed based on questionnaires developed in PROSPECT and reported in Deliverable 7.2. This wholistic approach allows an expert discussion on the potentials of the PROSPECT functions in the future

Assessment of the PROSPECT safety systems including socio-economic evaluation

This report provides a new methodology for safety benefit assessment of real-world benefit of the Advanced Driver Assistance Systems (ADAS) in terms of saved lives and prevented injuries as well as the resulting monetary benefit for society. This methodology is demonstrated and applied to PROSPECT systems that address potential crashes of passenger cars with vulnerable road users (VRUs) such as pedestrians and cyclists

Forward collision warning based on a driver model to increase drivers’ acceptance

Objective: Systems that can warn the driver of a possible collision with a vulnerable road user (VRU) have significant safety benefits. However, incorrect warning times can have adverse effects on the driver. If the warning is too late, drivers might not be able to react; if the warning is too early, drivers can become annoyed and might turn off the system. Currently, there are no methods to determine the right timing for a warning to achieve high effectiveness and acceptance by the driver. This study aims to validate a driver model as the basis for selecting appropriate warning times. The timing of the forward collision warnings (FCWs) selected for the current study was based on the comfort boundary (CB) model developed during a previous project, which describes the moment a driver would brake. Drivers’ acceptance toward these warnings was analyzed. The present study was conducted as part of the European research project PROSPECT (“Proactive Safety for Pedestrians and Cyclists”). Methods: Two warnings were selected: One inside the CB and one outside the CB. The scenario tested was a cyclist crossing scenario with time to arrival (TTA) of 4 s (it takes the cyclist 4 s to reach the intersection). The timing of the warning inside the CB was at a time to collision (TTC) of 2.6 s (asymptotic value of the model at TTA = 4 s) and the warning outside the CB was at TTC = 1.7 s (below the lower 95% value at TTA = 4 s). Thirty-one participants took part in the test track study (between-subjects design where warning time was the independent variable). Participants were informed that they could brake any moment after the warning was issued. After the experiment, participants completed an acceptance survey. Results: Participants reacted faster to the warning outside the CB compared to the warning inside the CB. This confirms that the CB model represents the criticality felt by the driver. Participants also rated the warning inside the CB as more disturbing, and they had a higher acceptance of the system with the warning outside the CB. The above results confirm the possibility of developing wellsaccepted warnings based on driver models. Conclusions: Similar to other studies’ results, drivers prefer warning times that compare with their driving behavior. It is important to consider that the study tested only one scenario. In addition, in this study, participants were aware of the appearance of the cyclist and the warning. A further investigation should be conducted to determine the acceptance of distracted drivers

Car-to-cyclist Forward Collision Warning effectiveness evaluation: a parametric analysis on reconstructed real accident cases

International audienceThe objective of the study is to quantify the benefits of an earlier brake activation by the drivers potentially achieved by a Forward Collision Warning (FCW) system in simulated car-to-cyclist accident scenarios. A parametric analysis is performed by varying the detection sensor Field Of View (FOV), the FCW trigger time and the driver's reaction lag time to the FCW. Almost two thousand and three hundred car-to-cyclist accidents are clustered in the following five main scenarios: crossing nearside (33%), crossing farside (22%), longitudinal (5%), turning left (12%) and turning right (22%). The remaining is clustered in Others group (6%). For all accident cases, original accident kinematics are processed through Matlab scripts from which FCW FOV, FCW trigger time and driver's reaction can be modified. The Matlab scripts return the new accident kinematics which can result in the accident being avoided or mitigated. This study shows that a 70° FOV, a FCW trigger time of 2.6s before the impact and a 0.6s driver's reaction to the FCW has a positive result in 82% of the accident cases with 78% being avoided and 4% mitigated. Concerning the parameters, the FOV has a greater influence on the avoidance rates compared to FCW trigger time and driver's reaction. Our study also reveals that FCW system has a higher benefit in the crossing farside scenario and a lower benefit in the turning right scenario. This paper highlights generic characteristics of FCW systems to optimize safety benefit for the different accident scenarios

Do drivers change their manual car-following behaviour after automated car-following?

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle