Giulio Francesco Bianchi Piccinini

A Review of Research on Driving Styles and Road Safety

Objective: The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety. Background: Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking. Method: A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety. Results: A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and sociocultural factors influencing driving style were reviewed. Conclusion: Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations. There is a considerable potential for increasing road safety by means of behavior modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles. Application: Knowledge about driving styles can be applied in programs for modifying driver behavior and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems.

Driver's Behavioral Adaptation to Adaptive Cruise Control (ACC): The Case of Speed and Time Headway

PROBLEM The Adaptive Cruise Control is an Advanced Driver Assistance System (ADAS) that allows maintaining given headway and speed, according to settings pre-defined by the users. Despite the potential benefits associated to the utilization of ACC, previous studies warned against negative behavioral adaptations that might occur while driving with the system activated. Unfortunately, up to now, there are no unanimous results about the effects induced by the usage of ACC on speed and time headway to the vehicle in front. Also, few studies were performed including actual users of ACC among the subjects. OBJECTIVES This research aimed to investigate the effect of the experience gained with ACC on speed and time headway for a group of users of the system. In addition, it explored the impact of ACC usage on speed and time headway for ACC users and regular drivers. METHOD A matched sample driving simulator study was planned as a two-way (2×2) repeated measures mixed design, with the experience with ACC as between-subjects factor and the driving condition (with ACC and manually) as within-subjects factor. RESULTS The results show that the usage of ACC brought a small but not significant reduction of speed and, especially, the maintenance of safer time headways, being the latter result greater for ACC users, probably as a consequence of their experience in using the system. SUMMARY The usage of ACC did not cause any negative behavioral adaptations to the system regarding speed and time headway. PRACTICAL APPLICATIONS Based on this research work, the Adaptive Cruise Control showed the potential to improve road safety for what concerns the speed and the time headway maintained by the drivers. The speed of the surrounding traffic and the minimum time headway settable through the ACC seem to have an important effect on the road safety improvement achievable with the system.

Great expectations: a predictive processing account of automobile driving

ABSTRACT Predictive processing has been proposed as a unifying framework for understanding brain function, suggesting that cognition and behaviour can be fundamentally understood based on the single principle of prediction error minimisation. According to predictive processing, the brain is a statistical organ that continuously attempts get a grip on states in the world by predicting how these states cause sensory input and minimising the deviations between the predicted and actual input. While these ideas have had a strong influence in neuroscience and cognitive science, they have so far not been adopted in applied human factors research. The present paper represents a first attempt to do so, exploring how predictive processing concepts can be used to understand automobile driving. It is shown how a framework based on predictive processing may provide a novel perspective on a range of driving phenomena and offer a unifying framework for traditionally disparate human factors models.

Effects on driving task and road safety impact induced by the usage of adaptive cruise control (ACC): A focus groups study

Adaptive cruise control (ACC), through the partial automation of the longitudinal driving task, is expected to increase driving comfort and assure a positive impact on road safety. Previous research showed that some side effects can result from the usage of the ACC. Those studies were mainly performed with drivers who never used the ACC before taking part in the experiments. For this reason, little information is available about how the ACC actually affects the driving task of real users of the system. In order to investigate the topic, two focus groups sessions were performed in Portugal with 13 ACC drivers. The findings revealed users’ satisfaction regarding the comfort and safety provided by the system. On the other hand, some cases of behavioural adaptations to the system were reported. Further research on the problem domain is suggested.

Adaptive human machine interface based on the detection of driver's cognitive state using machine learning approach

Cognitive distraction during the driving task might cause impairment of detection performance and of the recognition and/or response selection, increasing the risk of road crashes. In order to avoid or mitigate the negative effects related to cognitive distraction, this paper describes the development and testing of a Cooperative Lane Change Assistant (C-LCA) system: it takes into account the real-time drivers cognitive state by means of a cognitive distraction classifier expressly designed and it implements road cooperation between the vehicles thanks to a cooperative driver model. Three different test sessions were conducted on a static driving simulator and, in each test session, the participants carried out several analogous runs of a reference protocol test, derived from the Lane Change Task. Using the data collected during the first test session, the cognitive distraction classifier was developed using Machine Learning techniques. In the remaining two sessions, a specific C-LCA HMI prototype with visual and acoustic interfaces has been evaluated. The results show that the C-LCA reduced the workload during the lane change manoeuvres compared both with the baseline and with the assistance of a non-cooperative warning system. As well, the users expressed satisfaction about the Visual Interface and Acoustic Interface designed for the C-LCA.