*1.6. Evaluation*

Formerly the evaluation of driving behavior focused on the driver, and included criteria related to controllability, disturbance behavior, observability and parameter insensitivity. These criteria could be evaluated in a manageable amount of testing on proving grounds, often with open-loop maneuvers to exclude human vehicle guidance. In addition, human impression of the driving behavior and comfort was rated with different subjective and objective methods, leading into an evaluation of drivability of the vehicle. In driving automation, the driver increasingly transforms into a passenger, so we need to take into account the human as a co-driver or even a passenger, so rating becomes more of a co-drivability feature. Additional focus has to be put on other aspects such as perceived trust, safety and acceptance of the human occupant, which happens on a psychological level. However, the physiological aspect also has to be taken into account; for example, motion sickness as experienced often from passengers.

However, the sheer infinite amount of possible driving scenarios call for innovative methods for evaluating not only the safe behavior of an automated vehicle, but also a high rating of co-drivability.

#### **2. Articles of the Special Issue**

This Special Issue deals with recent advances related to the technological aspects of the aforementioned challenges:


The collection includes 15 articles that deal with the aforementioned challenges. In Figure 1, the thematic classification of the different articles related to the different system components is illustrated. Not surprisingly, it illustrates that many studies are focused on reliable human perception.

Article [1] deals with a methodology to quantify the performance of sensor models in virtual validation and verification (V & V) of automated driving functions, an important step towards reduction of on-road testing. The effect of automation on traffic flow during the transition phase in mixed traffic was investigated by [2]. Article [3] deals with the quality of ground truth annotation data to improve the transfer of on-road testing results into simulation. The evaluation of perceived trust was examined in [4], demonstrated in a driving simulator study. The topic of drowsiness classification in the context of driving automation was investigated in [5]. In simulation of Automated Driving (AD) functions, modelling of camera sensors is often carried out with physical modelling; however, research in [6] presented an alternative with phenomenological modeling. Article [7] introduced a conflicted managemen<sup>t</sup> framework, especially focusing on aiming at managing urban and peri-urban traffic. The potential of implementing AI into vehicle guidance, demonstrated on the safety of ACC, was investigated in [8]. Article [9] deals with insufficiencies during the decomposition of testing of ADAS functions from the system to lower levels, and defining rules for testing of modules to dispense with system tests. Improvement of vehicle control for wheel loaders was investigated in [10] using a deep learning-based prediction model of the throttle valve. The difficulties in reliable detection of pedestrians is addressed in [11], based on convolutional neural network algorithms applied on images manipulated with inverse gamma correction. Vehicle control at handling limits was investigated in [12], introducing a model-predictive controller that is able to initiate and maintain steadystate drifting. Article [13] deals with a functional prototype of a cooperative perception system aiming at future cloud-based services of automated driving functions, focusing on motorway use. A field study dealing with the capability of a market-introduced traffic sign recognition system was conducted in [14], revealing deficits by misreading of signs. A method to introduce automated driving functions in traffic flow simulation for virtual V & V was introduced by [15], based on a co-simulation framework between multi-body and traffic flow simulation.

As the Special Issue is dedicated to this topic, future research will continue in the development of the individual system components and their complex interaction, constantly rising the level of autonomy while providing an acceptable behavior for the individual and the society, superior compared to human vehicle guidance.

**Author Contributions:** Conceptualization, A.E., Z.S., M.F. and H.L.; writing—original draft preparation, A.E.; writing—review and editing, A.E., Z.S., M.F. and H.L.; visualization, A.E.; project administration, A.E. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable.

**Acknowledgments:** The editors express their thanks to the excellent and elaborative work of the international reviewers in evaluating the articles of this Special Issue.

**Conflicts of Interest:** The authors declare no conflict of interest.
