Most recent vehicles are equipped with touchscreens, which replace arrays of buttons that control secondary driving functions, such as temperature level, strength of ventilation, GPS, or choice of radio stations. While driving, manipulating such interfaces can be problematic in terms of safety, because they require the drivers’ sight. In this article, we develop an innovative interface, MovEcho, which is piloted with gestures and associated with sounds that are used as informational feedback. We compare this interface to a touchscreen in a perceptual experiment that took place in a driving simulator. The results show that MovEcho allows for a better visual task completion related to traffic and is preferred by the participants. These promising results in a simulator condition have to be confirmed in future studies, in a real vehicle with a comparable expertise for both interfaces.

In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase intelligibility by applying dedicated voice transformation techniques without changing the original SNR. The proposed method consists in an adaptive equalizer which reallocates the energy of frequency bands to maximize the Speech Intelligibility Index (SII) under the constraint of a fixed perceived loudness. The validation of the algorithm is carried out by means of a perceptual test derived from the Hearing in Noise Test (HINT) using four typical in-car noises of different driving conditions. The results obtained demonstrate the merit of the algorithm for low-frequency noises, that correspond to usual driving conditions, but also show the limit of the algorithm on noises with a spectrum more spread out induced by rain.

In this paper, we propose several approaches for neutral to Lombard speech conversion. We study in particular the influence of different recurrent neural network architectures where their main hyper-parameters are carefully selected using a bandit-based approach. We also apply the Continuous Wavelet Transform (CWT) as a multi-resolution analysis framework to better model temporal dependencies of the different features selected. The speech conversion results obtained are validated by means of objective evaluations which highlight in particular the interest of the wavelet transform for the learning process.

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.

In this paper, we propose a novel approach aiming at improving the intelligibility of speech in the context of in-car applications. Speech produced in noisy environments is subject to the Lombard effect which gathers a number of voice transformation effects compared to the speech produced in calm environments. To improve intelligibility of in car speech (radio, message alerts, ...), we propose to modify the original speech signal by incorporating one of the important Lombard effect, namely the shift of the lower formant center frequencies away from the competing noise regions. The proposed approach exploits traditional Linear Prediction analysis and overlap and add synthesis. We explore several modification strategies and the merit of each modification is evaluated using both objective and subjective tests. It is in particular shown that the improvement of speech intelligibility in car noise is significantly improved for a majority of listeners.

While modern dynamic driving simulators equipped with six degrees-of-freedom (6-DOF) hexapods and X-Y platforms have improved realism, mechanical limitations prevent them from offering a fully realistic driving experience. Solutions are often sought in the ”washout” algorithm, with linear accelerations simulated by an empirically chosen combination of translation and tilt-coordination, based on the incapacity of otolith organs to distinguish between inclination of the head and linear acceleration. In this study, we investigated the most effective combination of tilt and translation to provide a realistic perception of movement. We tested 3 different braking intensities (deceleration), each with 5 inverse proportional tilt/translation ratios. Subjects evaluated braking intensity using an indirect method corresponding to a 2-Alternative-Forced-Choice Paradigm. We find that perceived intensity of braking depends on the tilt/translation ratio used: for small and average decelerations (0.6 and 1.0m/s2), increased tilt yielded an increased overestimation of braking, inverse proportionally with intensity; for high decelerations (1.4m/s2), on half the conditions braking was overestimated with more tilt than translation and underestimated with more translation than tilt. We define a mathematical function describing the relationship between tilt, translation and the desired level of deceleration, intended as a supplement to motion cueing algorithms, that should improve the realism of driving simulations.