A conceptual model for persuasive in-vehicle technology to influence tactical level driver behaviour

Abstract

Persuasive in-vehicle systems aim to intuitively influence the attitudes and/or behaviour of a driver (i.e. without forcing them). However, the challenge in using these systems in a driving setting, is to maximise the persuasive effect without infringing upon the driver's safety. This paper proposes a conceptual model for driver persuasion at the tactical level (i.e., driver manoeuvring level, such as lane-changing and car-following). The main focus of the conceptual model is to describe how to safely persuade a driver to change his or her behaviour, and how persuasive systems may affect driver behaviour. First, existing conceptual and theoretical models that describe behaviour are discussed, along with their applicability to the driving task. Next, we investigate the persuasive methods used with a focus on the traffic domain. Based on this we develop a conceptual model that incorporates behavioural theories and persuasive methods, and which describes how effective and safe driver persuasion functions. Finally, we apply the model to a case study of a lane-specific advice system that aims to reduce travel time delay and traffic congestion by advising some drivers to change lanes in order to achieve a better distribution of traffic over the motorway lanes.

Supporting Drivers of Partially Automated Cars through an Adaptive Digital In-Car Tutor

“…To avoid driver overload, the length and type of DIT messages were adapted to the complexity of the driving situation. This could be considered a 'safety filter' for our DIT as described by Van Gent et al [29]. The communication was longer and more detailed in low complex situations, while it was condensed during highly complex situations.…”

“…Studies on the cognitive demands of feedback suggest that tutoring in highly complex driving situations should be condensed and action-based. Elaborate theory and reflection can be presented during low complexity situations [29][30][31]. Last, the feedback needs to adapt to the driver's performance, to update his or her mental model.…”

Analysing Noisy Driver Physiology Real-Time Using Off-the-Shelf Sensors: Heart Rate Analysis Software from the Taking the Fast Lane Project

“…This means advising drivers on where to drive. When interacting with a driver, the timing of messages to the driver is crucial not only for safety but also for the effectiveness of the advices [3]. Advising on those moments that workload is low and the driver can accommodate the advice, gives a higher chance of the driver following the advice.…”

“…Sensing when a driver is underloaded, overloaded, distracted or tired can improve safety by enabling dynamic adjustments in the way in-car systems interact with the driver. For example, by timing when navigational or other in-vehicle information systems relay information to the driver, or by adapting the content of their messages to match the current driver state, safety can be improved [3].…”

“…However, capturing and analysing heart rate in the -often noisy-conditions of either a simulator or an on-road setting can be difficult or costly [4]. The recent advances in wearable technology and open hardware platforms, such as the Arduino 2 and Raspberry Pi, 3 create new possibilities for collecting and analysing physiological data at low cost, given that validated algorithms exist to analyse and process it. In this paper we describe the development of such an algorithm named HeartPy, which we validated as described in [9].…”