With population aging and life expectancy increasing, elderly drivers have been increasing quickly in the United States and the heterogeneity among them with age is also increasingly non-ignorable. Based on traffic crash data of Pennsylvania from 2011 to 2019, this study was designed to identify this heterogeneity by quantifying the relationship between age and crash characteristics using linear regression. It is found that for elderly driver-involved crashes, the proportion leading to casualties significantly increases with age. Meanwhile, the proportions at night, on rainy days, on snowy days, and involving driving under the influence (DUI) decrease linearly with age, implying that elderly drivers tend to avoid traveling in risky scenarios. Regarding collision types, elderly driver-involved crashes are mainly composed of angle, rear-end, and hit-fixed-object collisions, proportions of which increase linearly, decrease linearly, and keep consistent with age, respectively. The increaseZhang, Zihao, Liu, Chenhui
This SAE Standard establishes minimum requirements for lighting and marking earthmoving work machinery as defined in SAE J1116. It may be used as guidance for other types of machinery. Earthmoving work machines are normally operated off-highway. Therefore, this SAE document is not intended to be used as a basis for regulations by those having authority over on-highway motor vehiclesOPTC3, Lighting and Sound Committee
This AS provides the minimum performance requirements for the following types of inflatable emergency evacuation devices (hereinafter referred to as device[s]): 1 Type I - Inflatable Slide: A device suitable for assisting occupants in descending from a floor-level airplane exit or from an airplane wing to the ground. A Type I off-wing slide is a device that does not include a ramp. 2 Type II - Inflatable Slide/Raft: A device suitable for assisting occupants in descending from a floor-level airplane exit or an airplane wing to the ground that is also designed to be used as a life raft. A Type II off-wing slide/raft is a device that does not include a ramp. 3 Type III - Inflatable Exit Ramp: A device suitable for assisting occupants in descending from certain overwing exits to an airplane wing. 4 Type IV - Inflatable Ramp/Slide: A device suitable for assisting occupants from an overwing exit or airplane wing to the ground. It is a combination ramp and wing-to-ground device. 5 Type VS-9A Safety Equipment and Survival Systems Committee
To investigate the rollover phenomena experienced by all-terrain vehicles (ATVs) during their motion caused by input from the road surface, a combined simulation using CarSim and Simulink has been employed to validate an active anti-rollover control strategy based on differential braking for ATVs, followed by vehicle testing. In the research process, a nonlinear three-degrees-of-freedom vehicle model has been developed. By utilizing a zero-moment point index as a rollover warning indicator, this approach could accurately detect the rollover status of the vehicle, particularly in scenarios involving low road adhesion on unpaved surfaces, which are characteristic of ATV operation. The differential braking, generating a roll moment by adjusting the amount of lateral force each braked tire can generate, was proved as an effective method to enhance rolling stability. Simulation and on-road testing results indicated that this control strategy effectively monitored the state of the ATV andHong, Hanchi, Wang, Kuan, d’Apolito, Luigi, Quan, Kangning, Yao, Xu
In order to improve the obstacle avoidance ability of autonomous vehicles in complex traffic environments, speed planning, path planning, and tracking control are integrated into one optimization problem. An integrated vehicle trajectory planning and tracking control method combining a pseudo-time-to-collision (PTC) risk assessment model and model predictive control (MPC) is proposed. First, a risk assessment model with PTC probability is proposed by considering the differentiation of the risk on the relative motion states of the self and front vehicles, and the obstacle vehicles in the lateral and longitudinal directions. Then, a three-degrees-of-freedom vehicle dynamics model is established, and the MPC cost function and constraints are constructed from the perspective of the road environment as well as the stability and comfort of the ego-vehicle, combined with the PTC risk assessment model to optimize the control. Finally, a complex multi-vehicle obstacle avoidance scenario isYang, Tao, Liu, Liang, Xu, Zhaoping
The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries. The most problematic of these are AIS 2+ and AIS 3+ upper-extremity and lower-extremityDalmotas, Dainius, Chouinard, Aline, Comeau, Jean-Louis, German, Alan, Robbins, Glenn, Prasad, Priya
Understanding left-turn vehicle-pedestrian accident mechanisms is critical for developing accident-prevention systems. This study aims to clarify the features of driver behavior focusing on drivers’ gaze, vehicle speed, and time to collision (TTC) during left turns at intersections on left-hand traffic roads. Herein, experiments with a sedan and light-duty truck (< 7.5 tons GVW) are conducted under four conditions: no pedestrian dummy (No-P), near-side pedestrian dummy (Near-P), far-side pedestrian dummy (Far-P) and near-and-far side pedestrian dummies (NF-P). For NF-P, sedans have a significantly shorter gaze time for left-side mirrors compared with light-duty trucks. The light-duty truck’s average speed at the initial line to the intersection (L1) and pedestrian crossing line (L0) is significantly lower than the sedan’s under No-P, Near-P, and NF-P conditions, without any significant difference between any two conditions. The TTC for sedans is significantly shorter than that forMatsui, Yasuhiro, Narita, Masashi, Oikawa, Shoko
The goal of this study was to gather and compare kinematic response and injury data on both female and male whole-body Post-mortem Human Surrogates (PMHS) responses to Underbody Blast (UBB) loading. Midsized males (50th percentile, MM) have historically been most used in biomechanical testing and were the focus of the Warrior Injury Assessment Manikin (WIAMan) program, thus this population subgroup was selected to be the baseline for female comparison. Both small female (5th percentile, SF) and large female (75th percentile, LF) PMHS were included in the test series to attempt to discern whether differences between male and female responses were predominantly driven by sex or size. Eleven tests, using 20 whole-body PMHS, were conducted by the research team. Preparation of the rig and execution of the tests took place at the Aberdeen Proving Grounds (APG) in Aberdeen, MD. Two PMHS were used in each test. The Accelerative Loading Fixture (ALF) version 2, located at APG’s Bear Point rangePietsch, Hollie, Cristino, Danielle, Danelson, Kerry, Bolte, John, Mason, Matthew, Kemper, Andrew, Cavanaugh, John, Hardy, Warren
Frontal-crash sled tests were conducted to assess submarining protection and abdominal injury risk for midsized male occupants in the rear seat of modern vehicles. Twelve sled tests were conducted in four rear-seat vehicle-bucks with twelve post-mortem human surrogates (PMHS). Select kinematic responses and submarining incidence were compared to previously observed performance of the Hybrid III 50th-percentile male and THOR-50M ATDs (Anthropomorphic Test Devices) in matched sled tests conducted as part of a previous study. Abdominal pressure was measured in the PMHS near each ASIS (Anterior Superior Iliac Spine), in the inferior vena cava, and in the abdominal aorta. Damage to the abdomen, pelvis, and lumbar spine of the PMHS was also identified. In total, five PMHS underwent submarining. Four PMHS, none of which submarined, sustained pelvis fractures and represented the heaviest of the PMHS tested. Submarining of the PMHS occurred in two out of four vehicles. In the matched tests, theGuettler, Allison J., Bianco, Samuel T., Albert, Devon L., Boyle, David M., Kemper, Andrew R., Hardy, Warren N.
THOR-AV 5F, a modified THOR-5F dummy, was designed to represent both upright and reclined occupants in vehicle crashworthiness studies. The dummy was evaluated in four test conditions: a) 25° seatback, 15 km/h, b) 25° seatback, 32 km/h, c) 45° seatback, 15 km/h, d) 45° seatback, 32 km/h. The dummy’s biomechanical responses were compared against those of postmortem human subjects (PMHS) tested in the same test conditions. The latest National Highway Traffic Safety Administration (NHTSA) BioRank method was used to provide a biofidelity ranking score (BRS) for each data channel in the tests to assess the dummy’s biofidelity objectively. The evaluation was categorized into two groups: restraint system and dummy. In the four test conditions, the restraint system showed good biofidelity with BRS scores of 1.49, 1.47, 1.15, and 1.79, respectively. The THOR-AV 5F demonstrated excellent biofidelity in three test conditions: 25° seatback, 15 km/h (BRS = 0.76); 25° seatback, 32 km/h (BRS = 0.89Wang, Z. Jerry, Humm, John, Hauschild, Hans W.
In this paper, experimental studies were conducted to examine the mechanical behavior of a polymer composite material called polyamide with glass fiber (PA6-GF), which was fabricated using the three-dimensional (3D) fusion deposition modeling (FDM) technique. FDM is one of the most well-liked low-cost 3D printing techniques for facilitating the adhesion and hot melting of thermoplastic materials. PA6 exhibits an exceptionally significant overall performance in the families of engineering thermoplastic polymer materials. By using twin-screw extrusion, a PA6-GF mixed particles made of PA6 and 20% glass fiber was produced as filament. Based on literature review, the samples have been fabricated for tensile, hardness, and flexural with different layer thickness of 0.08 mm, 0.16 mm, and 0.24 mm, respectively. The composite PA6-GF behavior is characterized through an experimental test employing a variety of test samples made in the x and z axes. The mechanical and physical characteristics ofSivanesh, A. R., Soundararajan, R., Natrayan, M., Nallasivam, J. D., Santhosh, R.
For taking counter measures in advance to prevent accidental risks, it is of significance to explore the causes and evolutionary mechanism of ship collisions. This article collects 70 ship collision accidents in Zhejiang coastal waters, where 60 cases are used for modeling while 10 cases are used for verification (testing). By analyzing influencing factors (IFs) and causal chains of accidents, a Bayesian network (BN) model with 19 causal nodes and 1 consequential node is constructed. Parameters of the BN model, namely the conditional probability tables (CPTs), are determined by mathematical statistics methods and Bayesian formulas. Regarding each testing case, the BN model’s prediction on probability of occurrence is above 80% (approaching 100% indicates the certainty of occurrence), which verifies the availability of the model. Causal analysis based on the backward reasoning process shows that H (Human error) is the main IF resulting in ship collisions. The causal chain that maximizesTian, Yanfei, Qiao, Hui, Hua, Lin, Ai, Wanzheng
Threat Analysis Risk Assessment (TARA) for automotive systems is standardized in ISO/SAE 21434. Traditionally these analyses have been bifurcated into either analysis focused on system functionality identifying impacts to assets based on the mission of the product, or analysis targeting vulnerabilities associated with the hardware and software of interfaces selected to be a part of a product. Furthermore, in the age of Software Defined Vehicles, the challenges to decouple use cases and the software that implements such from specific fixed hardware designs magnifies the disconnect between these risk methods. Use Case Based threat analysis, grounded in understanding features, stakeholders, and user stories, inherently yields security requirements tailored to specific functionalities and their contexts. While component-based threat analysis, derived from enumerations of vulnerabilities associated with interface choices, inherently yields security requirements tailored to specific defensesMazzara, Bill, Davidovich, Issak
The on-board emergency call system with accurate occupant injury prediction can help rescuers deliver more targeted traffic accident rescue and save more lives. We use machine learning methods to establish, train, and validate a number of classification models that can predict occupant injuries (by determining whether the MAIS (Maximum Abbreviated Injury Scale) level is greater than 2) based on crash data, and ranked the correlation of some factors affecting vehicle occupant injury levels in accidents. The optimal model was selected by the model prediction accuracy, and the Grid Search method was used to optimize the hyper-parameters for the model. The model is based on 2799 two-vehicle collision accident data from NHTSA CISS (The Crash Investigation Sampling System of NHTSA) traffic accident database.The results show that the model achieves high-precision prediction of occupant injury MAIS level (recall rate 0.8718, AUC(Area under Curve) 0.8579) without excluding vehicle model, andHuida, Zhang, Liu, Yu, Rui, Yang, Wu, Xiaofan, Fan, Tiqiang, WAN, XINMING
Objection detection using a camera sensor is essential for developing Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) vehicles. Due to the recent advancement in deep Convolution Neural Networks (CNNs), object detection based on CNNs has achieved state-of-the-art performance during daytime. However, using an RGB camera alone in object detection under poor lighting conditions, such as sun flare, snow, and foggy nights, causes the system's performance to drop and increases the likelihood of a crash. In addition, the object detection system based on an RGB camera performs poorly during nighttime because the camera sensors are susceptible to lighting conditions. This paper explores different pedestrian detection systems at low-lighting conditions and proposes a sensor-fused pedestrian detection system under low-lighting conditions, including nighttime. The proposed system fuses RGB and infrared (IR) thermal camera information. IR thermal cameras are used as they areThota, Bharath kumar, Somashekar, Karthik, Park, Jungme
Recent advancements towards autonomous heavy-duty vehicles are directly associated with increased interconnectivity and software driven features. Consequently, rise of this technological trend is bringing forth safety and cybersecurity challenges in form of new threats, hazards and vulnerabilities. As per the recent UN vehicle regulation 155, several risk-based security models and assessment frameworks have been proposed to counter the growing cybersecurity issues, however, the high budgetary cost to develop the tool and train personnel along with high risk of leakage of trade secrets, hinders the automotive manufacturers from adapting these third party solutions. This paper proposes an automated Threat Assessment & Risk Analysis (TARA) framework aligned with the standard requirements, offering an easy to use and fully customizable framework. The proposed framework is tailored specifically for heavy-duty vehicular networks and it demonstrates its effectiveness on a case study. TheMairaj ud din, Qazi, Ahmed, Qadeer
Plug-In Hybrid Vehicles (PHEV) have been of significant importance recently to comply with future CO2 and pollutant emissions limit. However, performance of these vehicles is closely related to the energy management strategy (EMS) used to ensure minimum fuel consumption and maximize electric driving range. While conventional EMS concepts are developed to operate in wide range of scenarios, this approach could potentially compromise the fuel consumption benefit due to the omission of route and traffic information. With the advancements in the availability of real-time traffic, navigation and driving route information, the EMS can be further optimized to extract the complete potential of a PHEV. In this context, this paper presents application of predictive energy management (PEM) functionalities combined with information such as live traffic data to reduce the fuel consumption for a P1/P3 configuration PHEV vehicle. The proposed PEM uses on-board navigation and E-horizon data based onLiu, Xuewu, Srivastava, Vivek, Pan, Wang, Schaub, Joschka, Sun, Jianqiang, Tian, Xi, Deng, Yunfei, Xiong, Jie, Wu, Xiaojun, Muthyala, Paul, Xu, Xiangyang
Cellular Vehicle-to-Everything (C-V2X) is considered an enabler for fully automated driving. It can provide the needed information about traffic situations and road users ahead of time compared to the onboard sensors which are limited to line-of-sight detections. This work presents the investigation of the effectiveness of utilizing the C-V2X technology for a valet parking collision mitigation feature. For this study a LiDAR was mounted at the FEV North America parking lot in a hidden intersection with a C-V2X roadside unit. This unit was used to process the LiDAR point cloud and transmit the information of the detected objects to an onboard C-V2X unit. The received data was provided as input to the path planning and controls algorithms so that the onboard controller can make the right decision while approaching the hidden intersection. FEV’s Smart Vehicle Demonstrator was utilized to test the C-V2X setup and the developed algorithms. Test results show that the vehicle was able toAlzu'bi, Hamzeh, Alrousan, Qusay, Obando, David, Rodriguez Zarazua, Pedro, Tasky, Tom
Lane detection plays a critical role in autonomous vehicles for safe and reliable navigation. Lane detection is traditionally accomplished using a camera sensor and computer vision processing. The downside of this traditional technique is that it can be computationally intensive when high quality images at a fast frame rate are used and has reliability issues from occlusion such as, glare, shadows, active road construction, and more. This study addresses these issues by exploring alternative methods for lane detection in specific scenarios caused from road construction-induced lane shift and sun glare. Specifically, a U-Net, a convolutional network used for image segmentation, camera-based lane detection method is compared with a radar-based approach using a new type of sensor previously unused in the autonomous vehicle space: radar retro-reflectors. This evaluation is performed using ground truth data, obtained by measuring the lane positions and transforming them into pixelBrown, Nicolas Eric, Patil, Pritesh, Sharma, Sachin, Kadav, Parth, Fanas Rojas, Johan, Hong, Guan Yue, DaHan, Liao, Ekti, Ali, Wang, Ross, Meyer, Rick, Asher, Zachary
Data-driven driving safety assessment is crucial in understanding the insights of traffic accidents caused by dangerous driving behaviors. Meanwhile, quantifying driving safety through well-defined metrics in real-world naturalistic driving data is also an important step for the operational safety assessment of automated vehicles (AV). However, the lack of flexible data acquisition methods and fine-grained datasets has hindered progress in this critical area. In response to this challenge, we propose a novel dataset for driving safety metrics analysis specifically tailored to car-following situations. Leveraging state-of-the-art Artificial Intelligence (AI) technology, we employ drones to capture high-resolution video data at 12 traffic scenes in the Phoenix metropolitan area. After that, we developed advanced computer vision algorithms and semantically annotated maps to extract precise vehicle trajectories and leader-follower relations among vehicles. These components, in conjunctionLu, Duo, Haines, Sam, Jammula, Varun Chandra, Rath, Prabin Kumar, Yu, Hongbin, Yang, Yezhou, Wishart, Jeffrey