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It is of great importance to monitor the driver’s status to achieve an intelligent and safe take-over transition in the level 3 automated driving vehicle. We present a camera-based system to recognise the non-driving activities (NDAs) which may lead to different cognitive capabilities for take-over based on a fusion of spatial and temporal information. The region of interest (ROI) is automatically selected based on the extracted masks of the driver and the object/device interacting with. Then, the RGB image of the ROI (the spatial stream) and its associated current and historical optical flow frames (the temporal stream) are fed into a two-stream convolutional neural network (CNN) for the classification of NDAs. Such an approach is able to identify not only the object/device but also the interaction mode between the object and the driver, which enables a refined NDA classification. In this paper, we evaluated the performance of classifying 10 NDAs with two types of devices (tablet and phone) and 5 types of tasks (emailing, reading, watching videos, web-browsing and gaming) for 10 participants. Results show that the proposed system improves the averaged classification accuracy from 61.0% when using a single spatial stream to 90.5%. [ABSTRACT FROM AUTHOR]

The physical bottleneck of optical design and the complexity associated with the human visual system (HVS) limits the true potential of the head-up display (HUD) for use in vehicles. A full windscreen of visual information feedback is required for driving safety, and its implementation to enhance the driving experience is one of the most significant challenges. We present an immersive augmented reality (AR) HUD concept to support future vehicle design following a human-centric design approach. The limited field of view of contemporary optical solutions can be overcome by multiple display elements tiled to utilize images over the entire windscreen and so create an immersive experience. The design takes important human factors into account and improves the operator’s driving experience. Furthermore, the images are “distributed,” meaning that the physical interface generates the images via multiple optical apertures/image sources and displays these images according to HVS requirements. These configurations are tested in a laboratory environment with a replica of a real car interior and a prototype vehicle installed with distributed multiple HUD units. The proposed concept of an immersive human-machine interface (HMI) can be further extended in various forms to other parts of the vehicle interior, including surfaces and free space, which we envisage will take place in future car designs. [ABSTRACT FROM AUTHOR]

In order to design an advanced human–automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper, a dynamic model of drivers’ neuromuscular interaction with a steering wheel is first established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver–steering–wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss–Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions, and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles. [ABSTRACT FROM AUTHOR]

Using interactive displays, such as a touchscreen, in vehicles typically requires dedicating a considerable amount of visual as well as cognitive capacity and undertaking a hand pointing gesture to select the intended item on the interface. This can act as a distractor from the primary task of driving and consequently can have serious safety implications. Due to road and driving conditions, the user input can also be highly perturbed resulting in erroneous selections compromising the system usability. In this paper, we propose intent-aware displays that utilize a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can be achieved with high confidence remarkably early in the pointing gesture. This can drastically reduce the time and effort required to successfully complete an in-vehicle selection task. In the proposed probabilistic inference framework, the likelihood of all the nominal destinations is sequentially calculated by modeling the hand pointing gesture movements as a destination-reverting process. This leads to a Kalman filter-type implementation of the prediction routine that requires minimal parameter training and has low computational burden; it is also amenable to parallelization. The substantial gains obtained using an intent-aware display are demonstrated using data collected in an instrumented vehicle driven under various road conditions. [ABSTRACT FROM PUBLISHER]

Emerging applications using body sensor networks (BSNs) constitute a new trend in car safety. However, the integration of heterogeneous body sensors with vehicular ad hoc networks (VANETs) poses a challenge, particularly on the detection of human behavioral states that may impair driving. This paper proposes a detector of human emotions, of which tiredness and stress (tension) could be related to traffic accidents. We present an exploratory study demonstrating the feasibility of detecting one emotional state in real time using a BSN. Based on these results, we propose middleware architecture that is able to detect emotions, which can be communicated via the onboard unit of a vehicle with city emergency services, VANETs, and roadside units, aimed at improving the driver's experience and at guaranteeing better security measures for the car driver. [ABSTRACT FROM PUBLISHER]