People park their vehicle depending on interior and exterior contexts. They do it naturally, even unconsciously. For instance, with a baby seat on the rear, the driver might leave more space on one side to be able to get the baby out easily; or when grocery shopping, s/he may position the vehicle for trunk accessibility. Autonomous vehicles are becoming technically effective at driving from A to B and parking in a proper spot, with a default way. However, in order to satisfy users' expectations and to become trustworthy, they will also need to park or make a temporary stop, appropriate to the given situation. In addition, users want to understand better the capabilities of their driving assistance features, such as automated parking systems. A voice-based interface can help with this and even ease the adoption of these features. Therefore, we developed a voice-based in-car assistant (Park4U Mate), that is aware of interior and exterior contexts (thanks to a variety of sensors), and that is able to park autonomously in a smart way (with a constraints minimization strategy). The solution was demonstrated to thirty-five users in test-drives and their feedback was collected on the system's decision-making capability as well as on the human-machine-interaction. The results show that: (1) the proposed optimization algorithm is efficient at deciding the best parking strategy; hence, autonomous vehicles can adopt it; (2) a voice-based digital assistant for autonomous parking is perceived as a clear and effective interaction method. However, the interaction speed remained the most important criterion for users. In addition, they clearly wish not to be limited on only voice-interaction, to use the automated parking function and rather appreciate a multi-modal interaction.

In order to increase road safety, among the visual and manual distractions, modern intelligent vehicles need also to detect cognitive distracted driving (i.e., the drivers mind wandering). In this study, the influence of cognitive processes on the drivers gaze behavior is explored. A novel image-based representation of the driver's eye-gaze dispersion is proposed to estimate cognitive distraction. Data are collected on open highway roads, with a tailored protocol to create cognitive distraction. The visual difference of created shapes shows that a driver explores a wider area in neutral driving compared to distracted driving. Thus, support vector machine (SVM)based classifiers are trained, and 85.2% of accuracy is achieved for a two-class problem, even with a small dataset. Thus, the proposed method has the discriminative power to recognize cognitive distraction using gaze information. Finally, this work details how this image-based representation could be useful for other cases of distracted driving detection.

The electrification of vehicles is without a doubt one of the milestones of today’s automotive technology. Even though industry actors perceive it as a future standard, acceptance, and adoption of this kind of vehicles by the end user remain a huge challenge. One of the main issues is the range anxiety related to the electric vehicle’s remaining battery level. In the scope of the H2020 ADAS&ME project, we designed and developed an intelligent Human Machine Interface (HMI) to ease acceptance of Electric Vehicle (EV) technology. This HMI is mounted on a fake autonomous vehicle piloted by a hidden joystick (called Wizard of Oz (WoZ) driving). We examined 22 inexperienced EV drivers during a one-hour driving task tailored to generate range anxiety. According to our protocol, once the remaining battery level started to become critical after manual driving, the HMI proposed accurate coping techniques to inform the drivers how to reduce the power consumption of the vehicle. In the following steps of the protocol, the vehicle was totally out of battery, and the drivers had to experience an emergency stop. The first result of this paper was that an intelligent HMI could reduce the range anxiety of the driver by proposing adapted coping strategies (i.e., transmitting how to save energy when the vehicle approaches a traffic light). The second result was that such an HMI and automated driving to a safe spot could reduce the stress of the driver when an emergency stop is necessary.