A Bayesian Reference Model for Visual Time-Sharing Behaviour in Manual and Automated Naturalistic Driving

Morando, Alberto; Victor, Trent; Dozza, Marco

doi:10.1109/tits.2019.2900436

Cited by 14 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Within each driving condition, a baseline has shorter off‐road glances than FM radio tuning, which in turn has shorter off‐road glances than USB song selection. These cumulative distributions are in line with previous findings (see [13, 14, 16, 21]).…”

Section: Methodssupporting

confidence: 93%

“…In two studies of a large‐scale naturalistic driving eye‐glance FOT dataset, no striking differences in aggregate off‐path glance duration distributions were found while using ACC compared to manual driving, rather the effect of ACC was characterised by longer glances on the path, yet there was a decrease in PRC (eyes on the path) [14]. Regarding visual time sharing, there was a slight tendency towards a higher median total task time with ACC active compared to manual driving [21]. Other studies have shown that the activation of risk management systems such as ACC and DA affects driver glance behaviour by producing glances off‐road tending to be longer and the percent of time spent looking at the road tends to be less [22, 23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

Bärgman

Victor

2019

IET Intelligent Transport Systems

Self Cite

View full text Add to dashboard Cite

Section: Methodssupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

Bärgman

Victor

2019

IET Intelligent Transport Systems

Self Cite

View full text Add to dashboard Cite

“…In the transportation domain, the Bayesian approach has been used to study road safety [24], such as crash estimation, prediction of road accidents, and road network safety evaluation [25][26][27][28]. In addition, Bayesian techniques have been employed to develop reference models [29] and to produce synthetic data that mimics driver behaviour [30].…”

Section: Current Studymentioning

confidence: 99%

Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data

Jokhio,

Olleja,

Bärgman

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Using turn signals to convey a driver's intention to change lanes provides a direct and unambiguous way of communicating with nearby drivers. Nonetheless, past research has indicated that drivers may not always use their turn signals before starting a lane change. In this study, realistic driving data are analyzed to investigate turn signal usage during lane changes on highways in and around Gothenburg, Sweden. Turn signal usage is examined and factors that influence it are identified by employing Bayesian hierarchical modelling. The study found that drivers used their turn signal before changing lanes in 60% of cases, after starting the lane change in 33% of cases, and did not use it at all in 7% of cases. The Bayesian hierarchical modelling results indicate that various factors, such as the speed and direction of lane changes and the presence of surrounding vehicles, influence the usage of turn signals. The study concludes that understanding the factors affecting turn signal usage is crucial for improving traffic safety in current and future mixed traffic with autonomous vehicles. The study discusses the implications of findings concerning increasing turn signal compliance through general policy‐making, improving existing in‐vehicle technologies and including turn signal usage in Pay‐As‐You‐Drive insurances.

show abstract

“…The higher prevalence of cell phone manual tasks in safety-critical event types -and in rear-end striking crashes specifically -is in agreement with previous research showing the importance of keeping eyes on the road, especially to avoid rear-end crashes (Victor, et al, 2014). The frequent co-occurrence of holding and texting in these event types suggests that the necessity for task interruption and visual time sharing during texting may make this task particularly risky (Tivesten & Dozza, 2014;Morando, Victor, & Dozza, 2019). MAD is particularly prevalent for rear-end striking crashes that are largely related to visual distraction.…”

Section: Approachmentioning

confidence: 99%

Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data

Bálint

Flannagan

Leslie

et al. 2020

Accident Analysis & Prevention

Self Cite

View full text Add to dashboard Cite

Objective: This paper 1) analyzes the extent to which drivers engage in multitasking additional-todriving (MAD) under various conditions, 2) specifies odds ratios (ORs) of crashing associated with MAD, and 3) explores the structure of MAD.Methods: Data from the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2 NDS) was analyzed to quantify the prevalence of MAD in normal driving as well as in safetycritical events of various severity level and compute point estimates and confidence intervals for the corresponding odds ratios estimating the risk associated with MAD compared to no task engagement. Sensitivity analysis in which secondary tasks were re-defined by grouping similar tasks was performed to investigate the extent to which ORs are affected by the specific task definitions in SHRP2. A novel visual representation of multitasking was developed to show which secondary tasks co-occur frequently and which ones do not.Results: MAD occurs in 11% of control driving segments, 22% of crashes and near-crashes (CNC), 26% of Level 1-3 crashes and 39% of rear-end striking crashes, and 9%, 16%, 17% and 28% respectively for the same event types if MAD is defined in terms of general task groups. The most common co-occurrences of secondary tasks vary substantially among event types; for example, "Passenger in adjacent seat -interaction" and "Other non-specific internal eye glance" tend to cooccur in CNC but tend not to co-occur in control driving segments. The odds ratios of MAD using SHRP2 task definitions compared to driving without any secondary task and the corresponding 95% confidence intervals are 2.38 (2.17-2.61) for CNC, 3.72 (3.11-4.45) for Level 1-3 crashes and 8.48 (5.11-14.07) for rear-end striking crashes. The corresponding ORs using general task groups to define MAD are slightly lower at 2.00 (1.80-2.21) for CNC, 3.03 (2.48-3.69) for Level 1-3 crashes and 6.94 (4.04-11.94) for rear-end striking crashes.Conclusions: The number of secondary tasks that the drivers were engaged in differs substantially for different event types. A graphical representation was presented that allows mapping task prevalence and co-occurrence within an event type as well as a comparison between different event types. The ORs of MAD indicate an elevated risk for all safety-critical events, with the greatest increase in the risk of rear-end striking crashes. The results are similar independently of whether secondary tasks are defined according to SHRP2 or general task groups. The results confirm that the reduction of driving performance from MAD observed in simulator studies is manifested in realworld crashes as well. © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ 2

show abstract

A Bayesian Reference Model for Visual Time-Sharing Behaviour in Manual and Automated Naturalistic Driving

Cited by 14 publications

References 38 publications

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance?

Exploring turn signal usage patterns in lane changes: A Bayesian hierarchical modelling analysis of realistic driving data

Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data

Contact Info

Product

Resources

About