Using current sensing technology, a wealth of data on driving sessions is potentially available through a combination of vehicle sensors and drivers' physiology sensors (heart rate, breathing rate, skin temperature, etc.). Our hypothesis is that it should be possible to exploit the combination of time series produced by such multiple sensors during a driving session, in order to (i) learn models of normal driving behaviour, and (ii) use such models to detect important and potentially dangerous deviations from the norm in real-time, and thus enable the generation of appropriate alerts. Crucially, we believe that such models and interventions should and can be personalised and tailor-made for each individual driver. As an initial step towards this goal, in this paper we present techniques for assessing the impact of cognitive distraction on drivers, based on simple time series analysis. We have tested our method on a rich dataset of driving sessions, carried out in a professional simulator, involving a panel of volunteer drivers. Each session included a different type of cognitive distraction, and resulted in multiple time series from a variety of on-board sensors as well as sensors worn by the driver. Crucially, each driver also recorded an initial session with no distractions. In our model, such initial session provides the baseline times series that make it possible to quantitatively assess driver performance under distraction conditions.
More simulator-based results for hand-held and hands-free mobile phone conversations are available from Haigney @cite_16 , using mean and standard deviation of heart rate, speed and variability of accelerator pedal travel before, during and after the call. Both groups of authors comment on compensatory behaviour of drivers while using a mobile phone. Yet another study in this space, by Rakauskas @cite_13 , also investigated the effects of mobile phone conversations on driving performance using a simulator as well as a similar set of measures, namely accelerator variability, speed variability, average speed, steering offset, mean lateral speed, reaction time, collisions and mental workload. Once again, the conclusion is that the effects of mobile phone conversations while driving are higher workloads and a decreased driving performance. Finally, the study by Drews @cite_7 is the closest to ours in terms of the situation and factors considered, i.e., the effects on driving performance of having passenger and mobile phones conversations, again assessed using a simulator. In this case, lane keeping, mean speed and mean distance were used for the assessment.
@cite_16 @cite_13 @cite_7
2066681138 2154998637 2146019564 2070420170
Abstract This paper reports a simulator-based study of the effects of mobile phone use on driving performance. Changes in heart rate indicated that mobile phone use increases the cognitive demand experienced by drivers with, it is argued, consequent reduction in safety margins. However, experimental results also suggested that participants engaged in a process of risk compensation, with driving speed being slower at times of mobile phone conversation while the number of off-road excursions (OFFS) and collisions remained stable. There also was some evidence that the use of a hand-held mobile phone (when compared to a hands-free system) was associated with poorer driving performance. Implications for real world' driving are considered. Problem: The prevalence of automobile drivers talking on cell phones is growing, but the effect of that behavior on driving performance is unclear. Also unclear is the relationship between the difficulty level of a phone conversation and the resulting distraction. Method: This study used a driving simulator to determine the effect that easy and difficult cell phone conversations have on driving performance. Results: Cell phone use caused participants to have higher variation in accelerator pedal position, drive more slowly with more variation in speed, and report a higher level of workload regardless of conversation difficulty level. Conclusions: Drivers may cope with the additional stress of phone conversations by enduring higher workloads or setting reduced performance goals. Impact on Industry: Because an increasing number of people talk on the phone while driving, crashes caused by distracted drivers using cell phones will cause disruptions in business, as well as injury, disability, and permanent loss of personnel. D 2004 National Safety Council and Elsevier Ltd. All rights reserved. This study examines how conversing with passengers in a vehicle differs from conversing on a cell phone while driving. We compared how well drivers were able to deal with the demands of driving when conversing on a cell phone, conversing with a passenger, and when driving without any distraction. In the conversation conditions, participants were instructed to converse with a friend about past experiences in which their life was threatened. The results show that the number of driving errors was highest in the cell phone condition; in passenger conversations more references were made to traffic, and the production rate of the driver and the complexity of speech of both interlocutors dropped in response to an increase in the demand of the traffic. The results indicate that passenger conversations differ from cell phone conversations because the surrounding traffic not only becomes a topic of the conversation, helping driver and passenger to share situation awareness, but the driving condition also has a direct influence on the complexity of the conversation, thereby mitigating the potential negative effects of a conversation on driving. We evaluate a mobile application that assesses driving behavior based on in-vehicle acceleration measurements and gives corresponding feedback to drivers. In the insurance business, such applications have recently gained traction as a viable alternative to the monitoring of drivers via "black boxes" installed in vehicles, which lacks interaction opportunities and is perceived as privacy intrusive by policyholders. However, pose uncertainty and other noise-inducing factors make smartphones potentially less reliable as sensor platforms. We therefore compare critical driving events generated by a smartphone with reference measurements from a vehicle-fixed IMU in a controlled field study. The study was designed to capture driver variability under real-world conditions, while minimizing the influence of external factors. We find that the mobile measurements tend to overestimate critical driving events, possibly due to deviation from the calibrated initial device pose. While weather and daytime do not appear to influence event counts, road type is a significant factor that is not considered in most current state-of-the-art implementations.