Introduction and Aims
While the perceived risks of driving under the influence of cannabis (DUIC) have been a focus of recent drug‐driving research, relevant concepts from the social cognition ...literature have rarely been applied to inform understanding of DUIC. This study aims to expand knowledge of perceived collision risk and social influences associated with DUIC and driving after other substance use.
Design and Methods
Semi‐structured interviews were conducted with 20 participants of a remedial program for impaired drivers. Thematic analysis began with two independent coders. Early discussion of emergent themes resulted in the identification of applicable social cognition concepts, resulting in selective coding and interpretation.
Results
Many participants identified DUIC as less risky than driving under the influence of alcohol or other drugs. Mixed perceptions regarding the dangerousness of DUIC were expressed, with some participants denying increased collision risk except among novice cannabis users. Comparative optimism bias was also expressed by participants who perceived themselves as less likely than others to be involved in a collision when DUIC. In view of normative influence, friends were generally seen as more accepting of DUIC than family, and there were indications that the opinions of others who use cannabis were regarded as more credible than the opinions of those who do not use the drug.
Discussion and Conclusions
Comparative optimism bias and normative influence may contribute to perceived risks associated with DUIC and may, therefore, be useful concepts to employ to increase the effectiveness of public health and road safety initiatives.
The issue of alcohol-impaired driving has received broad attention over the years, but drug-impaired driving also contributes to fatalities and injuries from traffic crashes. However, knowledge about ...the drug-impaired- driving problem is less advanced than for alcohol-impaired driving. This book discusses what is known about the extent of drug-impaired driving in the United States; challenges that exist for federal, state, and local agencies in addressing drug-impaired driving; and actions federal and state agencies have taken to address drug-impaired driving and what gaps exist in the federal response. This book also summarizes a series of studies undertaken by the National Highway Traffic Safety Administration to acquire the information needed to address the general problem of drug-impaired driving.
A large number of drivers with different driving characteristics co-exist on the road network. Assessing a person’s driving profile and detecting aggressive and unsafe driving behavior is essential ...to enhance road safety, reduce fuel consumption and – at a macroscopic level - tackle congestion. Nowadays, driving data can be massively collected via sensors embedded in mobile phones, avoiding the expensive and inefficient solutions of in-vehicle devices. In this paper, these data are used to detect unsafe driving styles based on two-stage clustering approach and using information on harsh events occurrence, acceleration profile, mobile usage and speeding. First, an initial clustering was performed in order to separate aggressive from non–aggressive trips. Subsequently, to distinguish “normal” trips from unsafe trips, a second level clustering was performed. In this way, trips have been categorized into six distinct groups with increasing importance with respect to safety. Findings reveal that about 50% of the trips were characterized as “safe trips”, while in 23.5% of the trips drivers were driving above the speed limit and only 7.5% of the trips are characterized by distracted driving. The further analysis of drivers in relation to the grouping of their trips showed that drivers cannot maintain a stable driving profile through time, but exhibit a strong volatile behavior per trip. Finally, a discussion is provided on the implications of the main findings in research and practice.
Road safety research has traditionally involved a focus on individuals in which social norms are considered but rarely discussed in detail. Outlining the existing body of research on young drivers in ...particular, In the Company of Cars shows the contribution that considering road safety from a social and cultural perspective could make to the reduction of death and injury on the roads. It highlights the involvement of driving cultures, as distinct from car cultures, in the social framing of cars and the ways in which they are utilised.
•An experimental test track study explored the impact of three deceleration approaches and a non-driving activity on passenger comfort in diverse driving scenarios.•A linear deceleration profile was ...compared to two stepwise profiles derived from a trained chauffeur’s driving data.•The linear deceleration was preferred for stops at Stop Signs, while smoother stepwise profiles were favoured overall.•Passenger engagement in non-driving activities didn’t affect comfort or profile preferences.•Participants reported perceiving a lower intensity of longitudinal vehicle movements when visually distracted during the drive.
As automated vehicles advance and become more widespread, it is increasingly important to ensure optimal driving comfort for passengers. Recent research has focused on developing driving styles for automated vehicles that are perceived to be most comfortable. However, there is still little understanding of whether, and how, possible driving styles need to be adjusted for specific traffic scenarios. In this study, 36 participants experienced three different deceleration profiles (a linear deceleration profile ‘One-Step’, and two versions of stepwise deceleration profiles ‘Two-Step V1 and V2’) across different driving scenarios (deceleration before curves, approaching a speed-limit sign, and a stop sign). Deceleration profiles were rated by participants and the impact of non-driving related activities on driving comfort was investigated. Results showed a positive rating for all deceleration profiles in terms of comfort. For decelerations to a standstill at a Stop Sign, participants seemed to prefer the One-Step approach, in which there is a continuous, and constant deceleration. However, participants described the Two-Step V1 as a gentle and calmer approach and ranked it more frequently as a personal favourite than the One-Step profile or the Two-Step V2 profile. The visual distraction of the passenger through a non-driving activity had no impact on passenger comfort or profile preferences for the scenarios tested within this study. Nonetheless, participants reported perceiving a lower intensity of longitudinal vehicle movements when visually distracted during the drive. The results of the study provide insights into the design and implementation of comfortable deceleration profiles.
It is unknown how many drivers are impaired by alcohol or cannabis with children as passengers (a situation known as driving under the influence child endangerment DUI-CE). This study examines the ...prevalence and patterns of alcohol and cannabis use among drivers with children on weekend nights and risk perceptions among these drivers.
Data came from 2,056 drivers (1,238 male) who participated in the Washington State Roadside Survey between June 2014 and June 2015. Oral fluid, blood, and breath samples were used to measure cannabis and alcohol use. Self-reported data were used to assess risk perceptions. Descriptive tabulations, weighted prevalence estimates, and chi-square tests were conducted.
Compared with other drivers, those who drove with a child were more likely to be driving during the daytime (46.6% vs. 36.3%, p = .03), less likely to be alcohol positive (0.2% vs. 4.5%, p < .0001), but as likely to be positive for Δ-9-tetrahydrocannabinol (THC) (14.1% vs. 17.7%, p = .29). Drivers with a child were less likely to report moderate to severe marijuana problems (3.3%) than those without a child (8.4%) (p < .02). Most drivers reported that cannabis use was very likely to impair driving. Among those who did not perceive any risk, 40.6% of drivers with a child and 28.9% of drivers without a child tested positive for THC.
Although most drivers with children did not drink and drive, many tested positive for cannabis, although it is unclear how many drivers may have been impaired. There is a need to examine driving situations that may put children at risks beyond those related to alcohol.
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•Cell phone texting during simulated driving increased the frequency and severity of Lane Excursions.•The frequency and severity of Lane Excursions were correlated with the duration ...of the texting task but not with driver age for those self-identified as non-skilled texters.•The frequency and severity of Lane Excursions were not correlated with the duration of the texting task, but were correlated with driver age for those self-identified as skilled texters.
Distracted driving is a significant contributor to motor vehicle accidents and fatalities, and texting is a particularly significant form of driver distraction that continues to be on the rise. The present study examined the influence of driver age (18–59 years old) and other factors on the disruptive effects of texting on simulated driving behavior. While ‘driving’ the simulator, subjects were engaged in a series of brief text conversations with a member of the research team. The primary dependent variable was the occurrence of Lane Excursions (defined as any time the center of the vehicle moved outside the directed driving lane, e.g., into the lane for oncoming traffic or onto the shoulder of the road), measured as (1) the percent of subjects that exhibited Lane Excursions, (2) the number of Lane Excursions occurring and (3) the percent of the texting time in Lane Excursions. Multiple Regression analyses were used to assess the influence of several factors on driving performance while texting, including text task duration, texting skill level (subject-reported), texting history (#texts/week), driver gender and driver age. Lane Excursions were not observed in the absence of texting, but 66% of subjects overall exhibited Lane Excursions while texting. Multiple Regression analysis for all subjects (N=50) revealed that text task duration was significantly correlated with the number of Lane Excursions, and texting skill level and driver age were significantly correlated with the percent of subjects exhibiting Lane Excursions. Driver gender was not significantly correlated with Lane Excursions during texting. Multiple Regression analysis of only highly skilled texters (N=27) revealed that driver age was significantly correlated with the number of Lane Excursions, the percent of subjects exhibiting Lane Excursions and the percent of texting time in Lane Excursions. In contrast, Multiple Regression analysis of those drivers who self-identified as not highly skilled texters (N=23) revealed that text task duration was significantly correlated with the number of Lane Excursions. The present studies confirm past reports that texting impairs driving simulator performance. Moreover, the present study demonstrates that for highly skilled texters, the effects of texting on driving are actually worse for older drivers. Given the increasing frequency of texting while driving within virtually all age groups, these data suggest that ‘no texting while driving’ education and public service messages need to be continued, and they should be expanded to target older drivers as well.
Objectives: How prevalent is drugged driving among Colorado drivers convicted of Driving Under the Influence (DUI)? What are the conviction rates of Colorado drivers charged with DUI, including ...impairment by marijuana's delta-9 tetrahydrocannabinol (THC)? Is Colorado's THC permissible inference law effective? To answer these questions, this report analyzes data published primarily in appendices of Colorado drugged driving reports.
Methods: In 2017 Colorado began requiring annual analyses of Driving Under the Influence (DUI) offenses, including causes and judicial consequences of DUI offenses. These analyses are performed by the Division of Criminal Justice's Office of Research and Statistics (ORS) within the Department of Public Safety. Each analysis requires ORS to link toxicology and court data bases. Data linking enables reporting of charges and convictions by categories including alcohol only, THC only, and polydrug use (two or more drugs simultaneously). Reports have been published annually for 5 years, the latest published in 2023 which covers case filings for 2020.
Results: A rough estimate of one-half of the state's DUI filings were attributed to drug use and half were attributed to alcohol only. The largest component of drugged driving was polydrug impairment, rather than impairment by a single drug like THC. Conviction rates in 2020 were 91% for alcohol only, 90% for polydrug cases, and 72% for THC only. Blood drug levels and law structure (per se, permissible inference, DUI definition) affected conviction rates significantly by defendant subsets. THC conviction rates in 2020 ranged from 11% to 100%, depending on blood drug levels and the legal charges.
Conclusions: Efforts to educate the public about the dangers of drugged driving should emphasize polydrug impairment, not simply THC impairment. States should analyze data on causes and consequences of DUI arrests to understand what their drugged driving problems are and what they are not. Non-zero drug per se levels and defining DUI as "incapable of safe driving" can severely reduce the effectiveness of DUI laws.
•37 participants, each overtook 7 cyclists on two lane road in a driving simulator.•We used these data to model how/when drivers decide to overtake and lateral clearance.•Higher driving speeds ...increase the probability to perform flying overtaking manoeuvres.•The lateral clearance predictive model results were not found to be satisfactory.•These models can support development and evaluation of active safety and policy making.
The involvement of cyclists in road crashes has not been decreasing with the same magnitude as the involvement of other road users. In particular, the interactions between cyclists and motorized traffic can lead to high-severity crashes. To improve the safety of these interactions, a thorough understanding of road user behaviour is first needed. In this study, we focused on drivers overtaking cyclists on rural roads. The two main objectives of this study were to develop models that predicted: (a) drivers’ decisions to perform either a flying or an accelerative overtaking manoeuvre in the presence of oncoming traffic, and (b) the lateral comfort distance that drivers maintain from cyclists during the overtaking.
A driving simulator study was designed to assess driver decision-making during the overtaking. The 37 drivers who participated in the study each performed seven overtaking manoeuvres with oncoming traffic. Out of the 259 overtaking manoeuvres, 168 were flying and 91 were accelerative. Binary logistic-regression models with mixed effects predicted the type of overtaking strategy (flying or accelerative). Driving speeds were found to significantly affect the strategy. The overall performance of the models predicting the strategy was 85–90%. Models were also developed for predicting the lateral comfort distance. The results show that the lateral comfort distance is mostly affected by the longitudinal distance between the subject vehicle and the oncoming vehicle, the longitudinal distance between the subject vehicle and the cyclist, and the presence of an oncoming vehicle—as well as by the drivers’ characteristics (sensation seeking in flying overtaking manoeuvres and ordinary violations in accelerative manoeuvres). The root mean square error, which was used to assess the performance of the models, ranged from 0.56 to 0.62.
In conclusion, the models predicting the overtaking strategy performed reasonably well, while the models predicting lateral distance did not provide accurate predictions. The models predicting overtaking strategy may support (1) the development and evaluation of active safety systems, (2) the design of automated driving, and (3) policy making.
Decision making for self-driving cars is usually tackled by manually encoding rules from drivers’ behaviours or imitating drivers’ manipulation using supervised learning techniques. Both of them rely ...on mass driving data to cover all possible driving scenarios. This study presents a hierarchical reinforcement learning method for decision making of self-driving cars, which does not depend on a large amount of labelled driving data. This method comprehensively considers both high-level manoeuvre selection and low-level motion control in both lateral and longitudinal directions. The authors firstly decompose the driving tasks into three manoeuvres, including driving in lane, right lane change and left lane change, and learn the sub-policy for each manoeuvre. Then, a master policy is learned to choose the manoeuvre policy to be executed in the current state. All policies, including master policy and manoeuvre policies, are represented by fully-connected neural networks and trained by using asynchronous parallel reinforcement learners, which builds a mapping from the sensory outputs to driving decisions. Different state spaces and reward functions are designed for each manoeuvre. They apply this method to a highway driving scenario, which demonstrates that it can realise smooth and safe decision making for self-driving cars.
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