Search Results

Abstract Potential collisions with oncoming traffic while turning left belong to the most safety-critical situations accounting for ~25% of all intersection crossing path crashes. A Left Turn Assist (LTA) was developed to reduce the number of crashes. Crucial for the effectiveness of the system is the design of the human-machine interface (HMI), i.e. defining how the system uses the calculated crash probability in the communication with the driver. A driving simulator study was conducted evaluating a warning strategy for two use cases: firstly, the driver comes to a stop before turning (STOP), and secondly, the driver moves on without stopping (MOVE). Forty drivers drove through three STOP and two MOVE scenarios. For the STOP scenarios, the study compared the effectiveness of an audio-visual warning with an additional brake intervention and a baseline. For the MOVE scenarios, the study analyzed the effectiveness of the audio-visual warning against a baseline.

Abstract Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

Abstract Purpose: Obesity can be a contributing factor to decrease head injuries in motor vehicle collisions (MVCs). We sought to assess whether obesity is associated with decreased head injury from MVCs by sex. Methods: This study was a retrospective observational study and evaluated crash data from 1997 to 2015 obtained from the International Center for Automotive Medicine. Patients were categorized into normal, overweight, and obesity subgroups. The primary endpoint was serious head injury. Multivariate logistic regression analysis was performed, and adjusted odds ratios (AORs) of subgroups were calculated for study outcomes adjusted for any potential confounders. Results: Among enrolled 588 patients, 262 were male with 30.9% normal, 40.1% overweight, and 29.0% obesity subgroups, and 326 were female with 44.8% normal, 24.8% overweight, and 30.4% obesity subgroups.

Abstract Modern driver compartment restraint systems have at least three key components that work together: safety belt system, airbags, and collapsible steering column. During a crash, a steering column will collapse at a predetermined force called breakaway force. Once the force of a crash has reached the breakaway force threshold, the column will move towards the motor area. When the column moves, the drivers’ peak forces and acceleration are decreased because the time and distance that are given to decelerate are increased. The usage of a breakaway force element inside the steering column allows car manufacturers to control the movement of the steering column at a certain point during a crash. Any load below the breakaway force, such as airbag deployment and normal or misuse forces applied by the driver, is absorbed by the system. Today’s force-based systems are optimized (design/configure) using various crash configurations, leading to one specific behavior of the column.

Abstract The frequency of video-capturing collision events from surveillance systems are increasing in reconstruction analyses. The video that has been provided to the investigator may not always include a clear perspective of the relevant area of interest. For example, surveillance video of an incident may have captured a pre- or post-incident perspective that, while failing to capture the precise moment when the pedestrian was struck by a vehicle, still contains valuable information that can be used to assist in reconstructing the incident. When surveillance video is received, a quick and efficient technique to place the subject object or objects into a three-dimensional environment with a known rate of error would add value to the investigation.

Abstract In recent years, to avoid the failure of the onboard perception system, intelligent vehicle infrastructure cooperative systems have been attracting attention in the field of autonomous vehicles. Using the perception technology of roadside sensors to provide supplementary traffic information for autonomous vehicles has become an increasing trend. Several roadside perception solutions select deep learning for three-dimensional (3D) object detection. However, deep learning methods have several issues and lack reliability in practical engineering applications. To tackle this challenge, this study proposes a pedestrian detection algorithm based on roadside Light Detection And Ranging (LiDAR) by combining traditional and deep learning algorithms. To meet real-time demand, Octree with region-of-interest (ROI) selection is introduced and improved to filter the background in each frame, which improves the clustering speed.

Abstract Due to their large volume structure, when a heavy vehicle encounters sudden road conditions, emergency turns, or lane changes, it is very easy for vehicle rollover accidents to occur; however, well-designed suspension systems can greatly reduce vehicle rollover occurrence. In this article, a novel semi-active suspension adaptive control based on AdaBoost algorithm is proposed to effectively improve the vehicle rollover stability under dangerous working conditions. This research first established a vehicle rollover warning model based on the AdaBoost algorithm. Meanwhile, the approximate skyhook damping suspension model is established as the reference model of the semi-active suspension. Furthermore, the model reference adaptive control (MRAC) system is established based on Lyapunov stability theory, and the adaptive controller is designed.

Abstract TOC

Abstract Numerous operational procedures regulate aerodrome ground traffic. Detailed solutions in these procedures often come from preventive recommendations formulated as a result of accident cause analysis. With time, the conclusions drawn based on incidents, i.e., events that did not result in material damage or casualties, are becoming increasingly significant. In this article, we propose a new method for determining the probability of an incident turning into an air accident, based on the example of aerodrome traffic operations. Premises conducive to an accident in the considered class of events depend on both human and physical factors. Thus a hybrid approach was applied. We used a fuzzy inference system to analyze the premises dependent on vehicle operators, while the simulation method was selected to examine the premises dependent on physical factors. Both were integrated using the technique of event trees with fuzzy probabilities (ETFP).

Abstract The driving safety performance of autonomous driving vehicles must be ensured before on-road implementation. Because it is not realistic to evaluate every single test condition in real-world traffic, computer simulation methods can be used. The driving safety performance can be evaluated by simulating various driving scenarios and calculating surrogate indicators representing dangerous collision risk. This study used a near-miss database and introduced a surrogate indicator that represents a potential risk in the driving scenarios for rear-end and cut-in collisions. The near-miss video database includes several driving scenarios experienced by human drivers, such as dangerous situations that lead to accidents, potentially dangerous situations that have a risk probability to escalate into dangerous situations, and normal driving situations. A skilled and attentive human driver foresees dangerous situations while driving and avoids them.

Abstract Safety is always a crucial aspect of developing autonomous systems, and the motivation behind this project comes from the need to address the traffic crashes occurring globally on a daily basis. The present work studies the coexistence of the novel rule-based behavioral planning framework with the five key advanced driver assistance system (ADAS) features as proposed in this article to fulfill the safety requirements and enhance the comfort of the driver/passengers to achieve a receding-horizon autopilot. This architecture utilizes data from the sensor fusion and the prediction module for the prediction time horizon of 2 s iteratively, which is continuously moving forward (hence, the receding horizon), and helps the behavior planner understand the intent of other vehicles on the road in advance.

Abstract The preeminent obligation of the automotive engineers, while designing a car, is to assure the driver’s well-being during any kind of impact by suppressing intrusions into the cockpit or minacious deceleration levels. Technologists and designers are advancing various modern active and passive safety systems to augment vehicle occupants’ safety. To mitigate the research and development expenditure in time and money, it is recommended to utilize computational crash simulations for the early evaluation of safety behavior under vehicle impact tests. Therefore, in this research study, an attempt is made to simulate crashworthiness and design the impact attenuator utilized in Formula SAE vehicles to absorb the kinetic energy of a car during a frontal collision. Closed-cell aluminum foam is selected as its material because of its less density than solid metals and ability to undergo large deformations at almost constant load.

Abstract Public discussions on the wide-scale implementation of connected and autonomous vehicles (CAVs) mostly rely on the media. As such, what is usually understood about “autonomous vehicle technology” by the general society is not necessarily equal to the current stage of the industrial production of “autonomous vehicles” in a similar timeline. To elaborate, society tends to have a misunderstanding about the technology where the autonomous vehicle (AV) is commonly perceived as a “vehicle with the highest level of automation” [1]. In reality, to enable a feasible, market-wide deployment of the technology, CAV technology, as a product, should be released to the market incrementally in a technologically realistic timeline. This unrealistic expectation and misunderstanding by the public, due to the aforementioned misapprehensions, subsequently puts impractical business expectations on engineering and development teams.

Abstract The growing interest from automakers and ride-hailing companies has increased the investment for high automation levels in vehicles. An important challenge in introducing autonomous vehicle (AV) technology to the market is the effort required in the validation. The research shows that AVs have to be test-driven hundreds of millions of miles to demonstrate reliability, which could take hundreds of years. Therefore, the identification of critical test scenarios and reduction of scenario sample space are urgent requirements for providing safe and reliable AVs in a time- and cost-efficient manner. This article proposes an AV test scenario generation system that creates abstract test scenarios using historical fatal accident data. The method processes and prunes the extensive fatal accident data to generate core test scenarios targeting the reasoning systems of AVs.

Abstract The Japanese population is aging rapidly, raising the number of traffic accidents involving elderly drivers. In Japan, single-vehicle accidents are a serious problem because they often result in fatalities. We analyzed these accidents by vehicle type, age group, and driving area. To examine the risk of accidents of the elderly drivers, their driving frequency needs to be considered, which is less. Moreover, it is difficult to know the actual distance driven by them. Therefore, in this paper, based on the assumption that the number of rear-end collisions is a proxy for the traffic volume, we used the number of such collisions as a control for the driving frequency. It was found that in single-vehicle accidents, elderly drivers were at higher risk than other age groups, especially when driving light motor vehicles (K-type vehicles) in non-urban areas.

Abstract The article considers the issues of a systemic approach to studying safety levels in transport operations and ways to increase the safety of the operator-machine system in Russian transport. The principal and problematic issues of reducing the risk of injury by preventing traffic accidents and reducing the severity of their impact have not been sufficiently addressed. When performing transport operations, there are often disagreements between the elements of the “Operator, Machine, and Environment” technological system due to the influence of external conditions and parameters of the constantly-changing environment in the workplace. This leads to a sharp increase in the number of failures of system elements, which reduces the level of safety of transport operations.

Abstract The crush energy is a key parameter to determine the delta-V in accident reconstructions. Since an accurate car crush profile can be obtained from 3D scanners, this research aims at validating the methods currently used in calculating crush energy from a crush profile. For this validation, a finite element (FE) car model was analyzed using various types of impact conditions to investigate the theory of energy-based accident reconstruction. Two methods exist to calculate the crush energy: the work based on the barrier force and the work based on force calculated by the vehicle acceleration times the vehicle mass. We show that the crush energy calculated from the barrier force was substantially larger than the internal energy calculated from the FE model. Whereas the crush energy calculated from the vehicle acceleration was comparable to the internal energy of the FE model.

Abstract The active anti-roll bar (AARB) system in vehicles has recently become one of the research hotspots in the field of vehicle technology to improve the vehicle’s active safety. In most off-road vehicles, high ground clearance is required while keeping all wheels in contact with the ground in order to improve traction and maintain load distribution among the wheels. A problem however arises in some types of the off-road vehicles when the vehicle is operated at high speeds on smooth roads. In such condition, the combination of the vehicle’s center of gravity position, large suspension stroke, and soft spring construction creates a stability problem, which could make the vehicle liable to rollover. This article analyzes a comparison of stability performance between passive and active anti-roll bar systems to improve rolling resistance. For active systems, two control strategies will be investigated. The conventional PID controller is firstly investigated and taken as a reference.

Abstract Cast magnesium (Mg) door inner panels can provide a good combination of weight, functional, manufacturing, and economical requirements. However, several challenges exist including casting technology for thin-wall part design, multi-material incompatibility, and relatively low strength versus steel. A project was supported by the US Department of Energy to design and develop a lightweight frame-under-glass door having a thin-wall, full die-cast, Mg inner panel. This development project is the first of its kind within North America. The 2.0 mm Mg design, through casting process enablers, has met or exceeded all stiffness and side-impact requirements, with significant mass reduction and part consolidation. In addition, a corrosion mitigation strategy has been established using industry-accepted galvanic isolation methods and coating technologies. The performance of the Mg design has been demonstrated through component and vehicle tests.

Abstract Braking strategy is the core of the automatic emergency braking (AEB) system. How to make the braking strategy of the AEB system more suitable for real traffic conditions and more acceptable to drivers is a very important research. This article carries out the braking strategy optimization for the AEB system. From the vehicle-pedestrian accidents collected by the National Automobile Accident In-depth Investigation System (NAIS) of Songjiang Station, this article selects typical cases equipped with event data recorder (EDR) and then analyzes the EDR data. On this basis, the strategy for the optimization of the AEB system is formed. The optimized strategy is simulated in the test scenario specified in the European New Car Assessment Programme (Euro-NCAP) and compared with the real vehicle test results. The result illustrates that the optimized braking strategy is more effective to avoid or mitigate collisions.