In 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.

https://www.mdpi.com/2079-9292/9/12/2087/pdf

Automated Driving: A Literature Review of the Take over Request in Conditional Automation

Distracted driving is one of the most common causes of traffic accidents around the world. Recognizing the driver’s gaze direction during a maneuver could be an essential step for avoiding the matter mentioned above. Thus, we propose a gaze zone classification system that serves as a base of supporting systems for driver’s situation awareness. However, the challenge is to estimate the driver’s gaze inside not ideal scenarios, specifically in this work, scenarios where may occur self-occlusions or non-aligned head and eyes direction of the driver. Firstly, towards solving miss classifications during self-occlusions scenarios, we designed a novel protocol where a 3D full facial geometry reconstruction of the driver from a single 2D image is made using the state-of-the-art method PRNet. To solve the miss classification when the driver’s head and eyes direction are not aligned, eyes and head information are extracted. After this, based on a mix of different data pre-processing and deep learning methods, we achieved a robust classifier in situations where self-occlusions or non-aligned head and eyes direction of the driver occur. Our results from the experiments explicitly measure and show that the proposed method can make an accurate classification for the two before-mentioned problems. Moreover, we demonstrate that our model generalizes new drivers while being a portable and extensible system, making it easy-adaptable for various automobiles.

A Robust Driver’s Gaze Zone Classification using a Single Camera for Self-occlusions and Non-aligned Head and Eyes Direction Driving Situations

Professional drivers are required to safely transport passengers and/or properties of customers to their destinations, so they must keep being mentally and physically healthy. Health problems will largely affect driving performance and sometimes cause loss of consciousness, which results in injury, death, and heavy compensation. Conventional systems can detect the loss of consciousness or urgently stop the vehicle to prevent accidents, but detection of symptoms of diseases and providing support before the driver loses consciousness is more reasonable. It is challenging to earlier detect symptoms with high confidence. Toward solving these problems, we propose a new method with a multi-sensor based driver monitoring system to detect cues of symptoms quickly and a verbal interaction system to confirm the internal state of the driver based on the monitoring results to reduce false positives. There is almost no data that records abnormal conditions while driving and tests with unhealthy participants are dangerous and ethically unacceptable, so we developed a system with pseudo-symptom data and did outlier detection only with normal driving data. From data collection experiments, we defined the confidence level derived from cue signs. The results of evaluation experiments showed that the proposed system worked well in pseudo headache and drowsiness detection scenarios. We found that signs of drowsiness varied with individual drivers, so the multi-sensor based driver monitoring system was proved to be effective. Moreover, we found that there were individual differences in how the cue signs appeared, so we can propose an online re-training method to make the system adapt to individual drivers.

/pdf/toward-health-related-accident-prevention-symptom-detection-4qp2gicm85.pdf

Toward Health–Related Accident Prevention: Symptom Detection and Intervention Based on Driver Monitoring and Verbal Interaction

In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

/pdf/development-of-a-situational-awareness-estimation-model-3x69dz54jo.pdf

Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations

In automated vehicles, driversare only required to input high-level control commands as opposed to lower-level commands in manually driven vehicles. The conventional driver-vehicle interfaces (DVIs) such as steering wheel and pedals that function in operational level, thus, may not be utilized in higher levels of automation. A DVI that allows the driver to input tactical-level control commands, i.e., lane change and turning, by easily understanding a situation, would be potentially required for automated vehicles. We thus propose tactical-level-interaction (TLI) for lateral and longitudinal controlling of highly automated vehicles. In this study, we developed a touchscreen-based DVI prototype that allows the driver to use simple touch gestures to input tactical control commands. The screen displays an augmented map including the ego vehicle rendered from the top view. The driver can instantly input a set of lateral commands by location-based TLI, e.g., lane changing, by designating a desired location on the map, e.g., lane, by double-tapping and swiping. Situational awareness is enhanced, for e.g., when approaching an intersection, by using visual and auditory prompts. We performed experiments using a simulator to evaluate TLI compared with the operational- (OLI, level 0) and strategical-level interaction (SLI, level 4). The results show that TLI offers both the flexibility of OLI as well as the comfort of SLI, and drivers prefer to use all three interaction methods depending on the driving environment. 

https://ieeexplore.ieee.org/ielx7/7274857/10076483/09693121.pdf

Preliminary Study of Tactical-Level Interaction for Highly-Automated Vehicles: Its Application to Touchscreen Interface

Highly-automated vehicles operating in level 3 issue a takeover request (TOR) to transfer the control authority from the autonomated driving (AD) system to a human driver when they encounter system limitations. In such ‘unscheduled’ situations, the driver is required to immediately re-engage in the driving task both physically and cognitively, and perform suitable action, e.g. lane change. Thus, evaluating driver engagement by the AD system would lead to safe takeover. Physical engagement is easily estimated but there are few studies on evaluating cognitive engagement. In this study, we thus developed a driver situational awareness estimation system based on glance information. We first defined seven standard glance areas and driver glance classification model using a convolutional neural network. We then obtained a large amount of glance data when both safe and dangerous takeover situations (lane change) by using a driving simulator, and we derived the standard glance model including the glance area and time, in order to estimate whether driver gained enough cognitive re-engagement in real-time. To evaluate the effectiveness of the proposed model, we created a situational awareness assist system to visually indicate regions with insufficient glance. As a result, we found that the assist system drastically improved driving performance and reduced the number of accidents during takeover.

A Driver Situational Awareness Estimation System Based on Standard Glance Model for Unscheduled Takeover Situations

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A Driver Situational Awareness Estimation System Based on Standard Glance Model for Unscheduled Takeover Situations