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Highly automated vehicles in urban areas will adopt a safe and cautious driving style, which is likely to result in frequent stops and yields while negotiating interactions in complex traffic environments. Users may not expect, comprehend or accept such behaviors from their vehicle. Considering that most daily trips are completed under time pressure, drivers may feel frustrated and decide to take over manual control in an attempt to save time. The current study aimed at investigating the impact of time pressure while driving a highly automated vehicle in urban areas in terms of anger and frustration. Forty participants drove a highly automated vehicle with or without time pressure in a driving simulator. The driving scenario included rural roads with light traffic and urban roads with traffic congestion and driving impediments. Data on mood, attitude and driver behavior were collected. Results indicate that urban automated driving in congested conditions deteriorated participants’ mood, independently of time pressure. Additionally, the number of takeovers on driver’s initiative was higher under time pressure. This study demonstrates that driving a highly automated vehicle in urban areas may have adverse effects for user’s emotional state. As long as drivers are uncomfortable with the vehicle behavior, they may decide to takeover in complex situations. Therefore, it is important to consider the emotional dimension of driving highly automated vehicles. Otherwise, expected benefits of automation for traffic congestion and safety may be compromised.

The complexity of urban driving environment may create frequent interruptions in the course of automated vehicles, which may have adverse effects on driver's mood, and even result in driver-initiated takeovers. A possible way to counteract this is to help drivers reappraise the situation by providing additional information when they encounter interruptions in vehicle's progression. This study aimed at assessing the effect of an HMI enhanced with contextual information on drivers' mood, attitude and behavior compared to a basic HMI. Participants drove the experimental scenario in a driving simulator, once with and once without receiving contextual information. Results are discussed, potential bias is identified, and perspectives are evoked to improve the proposed method.

International audience; Driver interaction with increasingly automated vehicles requires prior knowledge of system capabilities, operational know-how to use novel car equipment and responsiveness to unpredictable situations. With the purpose of getting drivers ready for autonomous driving, in a between-subject study sixty inexperienced participants were trained with an on-board video tutorial, an Augmented Reality (AR) program and a Virtual Reality (VR) simulator. To evaluate the transfer of training to real driving scenarios, a test drive on public roads was conducted implementing, for the first time in these conditions, the Wizard of Oz (WoZ) protocol. Results suggest that VR and AR training can foster knowledge acquisition and improve reaction time performance in takeover requests. Moreover, participants' behavior during the test drive highlights the ecological validity of the experiment thanks to the effective implementation of the WoZ methodology.

In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a take-over request when the system reaches its functional boundaries. In this context, Virtual Reality systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In this study, the effectiveness of an Head-Mounted display (HMD)-based training program for acquiring interaction skills in automated cars was compared to a user manual and a fixed-base simulator. Results show that the training system affects the take-over performances evaluated in a test drive in a high-end driving simulator. Moreover, self-reported measures indicate that the HMD-based training is preferred with respect to the other systems., Extended abstract for poster presentation at IEEE VR 2018

International audience; In conditionally automated vehicles, drivers can engage in secondary activities while traveling to their destination. However, drivers are required to appropriately respond, in a limited amount of time, to a takeover request when the system reaches its functional boundaries. Interacting with the car in the proper way from the first ride is crucial for car and road safety in general. For this reason, it is necessary to train drivers in a risk-free environment by providing them the best practice to use these complex systems. In this context, Virtual Reality (VR) systems represent a promising training and learning tool to properly familiarize drivers with the automated vehicle and allow them to interact with the novel equipment involved. In addition, Head-Mounted Display (HMD)-based VR (light VR) would allow for the easy deployment of such training systems in driving schools or car dealerships. In this study, the effectiveness of a light Virtual Reality training program for acquiring interaction skills in automated cars was investigated. The effectiveness of this training was compared to a user manual and a fixed-base simulator with respect to both objective and self-reported measures. Sixty subjects were randomly assigned to one of the systems in which they went through a training phase followed by a test drive in a high-end driving simulator. Results show that the training system affects the takeover performances. Moreover, self-reported measures indicate that the light VR training is preferred with respect to the other systems. Finally, another important outcome of this research is the evidence that VR plays a strategic role in the definition of the set of metrics for profiling proper driver interaction with the automated vehicle.

International audience; This paper presents a preliminary study of the use of Virtual Reality for the simulation of a particular driving task: the control recovery of a semi-autonomous vehicle by a driver engaged in an attention-demanding secondary activity. In this paper the authors describe a fully immersive simulator for semi-autonomous vehicles and present the pilot study that has been conducted for determining the most appropriate interface to interact with the simulator. The interaction with the simulator is not only limited to the actual car control; it also concerns the execution of a secondary activity which aims to put the driver out of the loop by distracting him/her from the main driving task. This study evaluates the role of a realistic interface and a 6-DoF controller-based interaction on objective and subjective measures. Preliminary results suggest that subjective indicators related to comfort, ease of use and adaptation show a significant difference in favor of realistic interfaces. However, task achievement performances do not provide decisive parameters for determining the most adequate interaction modality.