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Concerns about the risk of traffic collision injury to both police officers and bystanders are increasing as the use of in-vehicle technologies becoming widespread among agencies. This study used national and California data to characterize traffic collisions in which a police vehicle was involved. The paper aims to discuss these issues.

The authors used a California traffic collision database to retrospectively identify collisions that involved police vehicles for years 2007-2010. The authors summarized collision characteristics with descriptive methods and used log-binomial regression to estimate associations between personal and collision characteristics with officer culpability.

The authors identified 5,233 traffic collisions in California. In total, 10 percent of law enforcement vehicles were motorcycles. In all, 9 percent of cruisers struck a pedestrian or bicyclist, compared with only 2 percent of motorcycles. Compared with officers aged 50 or older, officers in younger age categories were progressively much more likely to have been culpable. Motorcycle officers were 33 percent less likely to be culpable for their collision involvements. Approximately 100 fatal collisions involving a law enforcement vehicle occur each year in the USA.

The findings from this study indicate that approximately 1,300 injury-producing traffic collisions occur each year in California that involve a law enforcement vehicle. The authors found that younger age, female sex, cruiser operation, traveling unbelted, and single-vehicle collision involvement were positively associated with officer culpability. Officer race and community population were not significantly associated with culpability. The occurrence of fatal collisions in the USA was stable over a 12-year period.

The purpose of this paper is to examine the situational and individual officer characteristics of officer-involved vehicle collisions that result in fatality, injury, and non-injury outcomes.

Data on 35,840 vehicle collisions involving law enforcement officers in California occurring between January 2000 and December 2009 are examined. A descriptive analysis of collision characteristics is presented.

There were 39 officers killed by collisions over this study period and 7,684 officers who received some type injury. Incidents involving officers on motorcycles represented 39 percent of officer fatalities and 39 percent of severe injuries. In the case of fatalities, 33 percent of officers were reported as wearing seatbelts, 38 percent were not wearing a seatbelt, and seatbelt use was not stated in 29 percent of car fatalities.

The findings only represent one state and the analysis is based on an estimated 86 percent of collisions that occurred during the study period due to missing data. Nonetheless, the results are based on a robust sample and address key limitations in the existing literature.

During the study period in California the estimated financial impact of collisions reached into the hundreds of millions of dollars when considering related fatality, injury, and vehicle damage costs combined. These impacts highlight the need for the law enforcement community to give greater attention to this issue.

At the time of this writing there was no published independent research that compares the situational and officer characteristics across fatality, injury, and non-injury outcomes in these events. The findings reported here will help inform emerging interest in this issue within the law enforcement, academic, and policy-making communities.

The presentation of in-vehicle warnings information at risky driving scenarios is aimed to improve the collision avoidance ability of drivers. Existing studies have found that driver’s collision avoidance performance is affected by both warning information and driver’s workload. However, whether moderation and mediation effects exist among warning information, driver’s cognition, behavior and risky avoidance performance is unclear.

This purpose of this study is to examine whether the warning information type modifies the relationship between the forward collision risk and collision avoidance behavior. A driving simulator experiment was conducted with waring and command information.

Results of 30 participants indicated that command information improves collision avoidance behavior more than notification warning under the forward collision risky driving scenario. The primary reason for this is that collision avoidance behavior can be negatively affected by the forward collision risk. At the same time, command information can weaken this negative effect. Moreover, improved collision avoidance behavior can be achieved through increasing drivers’ mental workload.

The proposed model provides a comprehensive understanding of the factors influencing collision avoidance behavior, thus contributing to improved in-vehicle information system design.

The significant moderation effects evoke the fact that information types and mental workloads are critical in improving drivers’ collision avoidance ability. Through further calibration with larger sample size, the proposed structural model can be used to predict the effect of in-vehicle warnings in different risky driving scenarios.

Little is known regarding the impact of organizational policies and practices on police officers’ driving behaviors. To address an important gap in the empirical literature, this study examined how perceived likelihood of discipline for violations of agency driving policies impacted officer-involved vehicle collisions.

Surveys were distributed to patrol officers and their supervisors in eight California law enforcement agencies. The surveys elicited information regarding the perceived likelihood of discipline for violations of agency driving policies regarding cell phone use, text messaging, seatbelt use, speeding, and vehicle operations during emergency and pursuit situations.

The findings demonstrated a significant impact of perceived likelihood of enforcement for some but not all agency driving policies on officer-involved vehicle collisions.

This study was limited to self-reported data from patrol officers and their supervisors in eight California agencies.

Findings suggest that agencies may reduce officer injuries and other costs by increasing supervision and enforcement of agency driving policies.

This study contributes to the extant body of literature on officer-involved vehicle collisions by considering the impact of agency policy and supervision on officer behavior.

This paper aims to investigate the marine vehicle accident characteristics in the inland waterways of Bangladesh, with particular emphasis on collision type accidents.

Accident data were collected from different sources such as the daily newspapers, reports of Department of Shipping (DOS) and Bangladesh Inland Water Transport Authority (BIWTA). The accidents are analysed according to different variables such as vehicle type, time distribution, spatial distribution, weather condition, nature of casualties, and others.

The study revealed that the leading causes of accidents were collision between ships, trawlers and country boats and loss of stability due to Nor'wester (a seasonal storm that appears from the north and western directions). Of the accidents, 80 per cent involve cargo vehicles hitting other vehicles. Collisions have increased dramatically over the last decade and the number of fatalities has increased significantly.

Many accidents in Bangladesh often remain under‐reported owing to limited exposure to the media, which is probably because the victims represent the poorer segment of society. This under‐reporting appears to be a major barrier for accident analysis and prevention.

The paper provides an explicit idea on the characteristics of accidents, so that the problem can be easily comprehended and necessary action can be taken by different stakeholders, such as operators, law enforcement agencies, designers, users and others concerned in order to come up with a coordinated action plan to stop these severe incidents.

The purpose of this paper is to propose a novel curvilinear path estimation model employing multivariate adaptive regression splines (MARS) for mid vehicle collision avoidance. The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase. This arrangement significantly narrows the gap between the estimated and the true path analyzed using the mean square error (MSE) for different offsets on Next Generation Simulation Interstate 80 (NGSIM I-80) data set. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation, thereby, making it amicable for real-road scenarios.

The two-phase path estimation scheme initially uses the offset (position) value of the front and the mid (host) vehicle to build the crisp model. The resulting crisp model is MARS regressed to deliver a closely aligned actual model in the second phase.

This arrangement significantly narrows the gap between the estimated and the true path studied using MSE for different offsets on real (Next Generation Simulation-NGSIM) data. The presented model also covers parallel parking by encompassing the reverse motion of the host vehicle in the path estimation. Thereby, making it amicable for real-road scenarios.

This paper builds a mathematical model that considers the offset and host (mid) vehicles for appropriate path fitting.

Automated driving systems (ADSs) are being developed to avoid human error and improve driving safety. However, limited focus has been given to the fallback behavior of automated vehicles, which act as a fail-safe mechanism to deal with safety issues resulting from sensor failure. Therefore, this study aims to establish a fallback control approach aimed at driving an automated vehicle to a safe parking lane under perceptive sensor malfunction.

Owing to an undetected area resulting from a front sensor malfunction, the proposed ADS first creates virtual vehicles to replace existing vehicles in the undetected area. Afterward, the virtual vehicles are assumed to perform the most hazardous driving behavior toward the host vehicle; an adaptive model predictive control algorithm is then presented to optimize the control task during the fallback procedure, avoiding potential collisions with surrounding vehicles. This fallback approach was tested in typical cases related to car-following and lane changes.

It is confirmed that the host vehicle avoid collision with the surrounding vehicles during the fallback procedure, revealing that the proposed method is effective for the test scenarios.

This study presents a model for the path-planning problem regarding an automated vehicle under perceptive sensor failure, and it proposes an original path-planning approach based on virtual vehicle scheme to improve the safety of an automated vehicle during a fallback procedure. This proposal gives a different view on the fallback safety problem from the normal strategy, in which the mode is switched to manual if a driver is available or the vehicle is instantly stopped.

Vehicle crashes and being struck by vehicles are the leading causes of death among police. The purpose of this paper is to identify risk factors for injury in police officer vehicle crashes in order to help determine the most effective approaches to improve officer vehicle safety.

The study entailed a cross-sectional survey of officer drivers involved in vehicle crashes from 16 local, county, and state law enforcement agencies across the USA over one year. The relative risk of injury for officers in crashes with a given characteristic relative comparison crashes without that characteristic was computed to determine which characteristics are more likely to be associated with injuries.

The survey yielded 854 crashes, 90 of which involved injuries to the officer driver. Crash characteristics associated with a statistically significant increase in the risk of injury include multiple vehicle collisions, collision direction, officer vehicle type, officer vehicle being stopped, driving under emergency conditions, conducting traffic control or assisting motorists, not wearing a seat belt, and others. Most findings hold for all crashes and when minor crashes are excluded from the analysis.

This study presents the first quantitative estimates of the risk factors for injury to law enforcement officers in vehicle crashes. Our findings indicate that seat belt use remains a critical safety intervention; driving under emergency conditions is high risk, though the reasons for this are unclear; better practices are needed to protect officers in stationary vehicles; agencies should carefully weigh the benefit of motorcycles against the vastly increased risk of injury they present; and that mobile data terminals are both a major distraction hazard and important source of injuries in crashes.

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.

The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles. A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads. With different steering and braking maneuvers, minimum safe distances were modeled and calculated. Considering safety and ergonomics, the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change. Furthermore, a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability. Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.

The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks. The proposed trajectory model could provide safety lane-change path planning, and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.

This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles. There are two main contributions: the first is a more quantifiable trajectory model for self-driving articulated vehicles, which provides the opportunity to adapt vehicle and scene changes. The second involves designing a feedback linearization controller, combined with a multi-objective decision-making mode, to improve the comprehensive performance of intelligent vehicles. This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.