Integration of driver support functions: the driver's point of view

Integration of driver support functions is a key issue in the development of in-vehicle systems that assist the driver with the driving task. This paper discusses a user needs survey that provides more insight into this issue from the perspective of the driver. Car drivers are asked to indicate their needs for driver assistance during certain driving tasks (e.g. congestion driving) and circumstances (e.g. reduced visibility). From this, consequences for the integration of functions can be deduced with respect to technology, HMI and functional operation. Preliminary results of a pilot test of the user needs survey are highlighted in this paper. These results indicate starting points for integrated driver assistance, such as the adaptability of systems based on personal needs for support, and the functional integration of driver support functions, for instance with respect to inter-vehicle communication

Work domain analysis and intelligent transport systems: Implications for vehicle design

This article presents a Work Domain Analysis (WDA) of the road transport system in Victoria, Australia. A series of driver information requirements and tasks that could potentially be supported through the use of Intelligent Transport Systems (ITS) are then extracted from the WDA. The potential use of ITS technologies to circumvent these information gaps and provide additional support to drivers is discussed. It is concluded that driver information requirements are currently not entirely satisfied by contemporary vehicle design and also that there are a number of driving tasks that could be further supported through the provision of supplementary systems within vehicles

Providing over-the-horizon awareness to driver support systems

Vehicle-to-vehicle communications is a promising technique for driver support systems to increase traffic safety and efficiency. A proposed system is the Congestion Assistant [1], which aims at supporting drivers when approaching and driving in a traffic jam. Studies have shown great potential for the Congestion Assistant to reduce the impact of congestion, even at low penetration. However, these studies assumed complete and instantaneous availability of information regarding position and velocity of vehicles ahead. In this paper, we introduce a system where vehicles collaboratively build a so-called TrafficMap, providing over-the-horizon awareness. The idea is that this TrafficMap provides highly compressed information that is both essential and sufficient for the Congestion Assistant to operate. Moreover, this TrafficMap can be built in a distributed way, where only a limited subset of the vehicles have to alter it and/or forward it in the upstream direction. Initial simulation experiments show that our proposed system provides vehicles with a highly compressed view of the traffic ahead with only limited communication

Novel Multimodal Feedback Techniques for In-Car Mid-Air Gesture Interaction

This paper presents an investigation into the effects of different feedback modalities on mid-air gesture interaction for infotainment systems in cars. Car crashes and near-crash events are most commonly caused by driver distraction. Mid-air interaction is a way of reducing driver distraction by reducing visual demand from infotainment. Despite a range of available modalities, feedback in mid-air gesture systems is generally provided through visual displays. We conducted a simulated driving study to investigate how different types of multimodal feedback can support in-air gestures. The effects of different feedback modalities on eye gaze behaviour, and the driving and gesturing tasks are considered. We found that feedback modality influenced gesturing behaviour. However, drivers corrected falsely executed gestures more often in non-visual conditions. Our findings show that non-visual feedback can reduce visual distraction significantl

microSlotted 1-Persistence Flooding in VANETs

Many Driver Support Systems in future vehicles will rely on wireless communication. This wireless communication can be divided into two categories: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V is often used for vehicles to exchange information of a local nature, e.g. co-operative following or collision avoidance. V2I can be used as ’smart road signs’, access to back-end networks (e.g. Internet) or as simple repeaters. The term VANET is key to V2V and V2I communication: Vehicular Ad hoc Network. A Driver Support System described in [1] presents an interesting problem: a vehicle should be aware of the state of traffic on a road, up to several kilometers ahead. A system called the TraffiFilter has been proposed in [2] to provide this information

Evaluation of ADAS with a supported-driver model for desired allocation of tasks between human and technology performance

Partly automated driving is relevant for solving mobility problems, but also causes concerns with respect to the driver‟s reliability in task performance. The supported driver model presented in this paper is therefore intended to answer the question, what type of support and in which circumstances, will enhance the driver‟s ability to control the vehicle. It became apparent that prerequisites for performing tasks differ per driving task‟s type and require different support. The possible support for each driving task‟s type, has been combined with support-types to reduce the error causations from each different performance level (i.e. knowledge-based, rule-based and skill-based performance). The allocation of support in relation to performance level and driving task‟s type resulted in a supported driver model and this model relates the requested circumstances to appropriate support types. Among three tested ADAS systems, semi-automated parking showed best allocation of support; converting the demanding parallel parking task into a rather routine-like operation

The psychology of driving automation: A discussion with Professor Don Norman

Introducing automation into automobiles had inevitable consequences for the driver and driving. Systems that automate longitudinal and lateral vehicle control may reduce the workload of the driver. This raises questions of what the driver is able to do with this 'spare' attentional capacity. Research in our laboratory suggests that there is unlikely to be any spare capacity because the attentional resources are not 'fixed'. Rather, the resources are inextricably linked to task demand. This paper presents some of the arguments for considering the psychological aspects of the driver when designing automation into automobiles. The arguments are presented in a conversation format, based on discussions with Professor Don Norman. Extracts from relevant papers to support the arguments are presented

An assisted driver model. Towards developing driver assistance systems by allocating support dependent on driving situations

Partially automated driving is expected to increase traffic efficiency. How-ever, automation causes human factors concerns. One concern is the reduced operability during transitions between automation level, e.g. when failures occur. These concerns ask for a more justifiable implementation of automation for automobile appliances. As a first step towards applicable solutions for driver support, we developed the assisted driver model. The attempt with this model was to answer: what driving situations are in need for what kind of support? The influence of different levels of automation on task performance, were used to define 7 recommended support types relevant for driver assistance. For the allocation of recommended support types to distinguished driving situations we then considered the prerequisites to provide good operability in terms of the avoidance of errors and familiarity with driving circumstances. An assessment of adaptive cruise con-trol showed the model‟s potential to help developing advanced driver assistance systems whilst anticipating concerns associated with the appliance of partial automation