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The UN R155 regulation is the first automotive cyber security regulation and has made security a mandatory approval criterion for new vehicle types. This establishes internationally harmonized security requirements for market approval. As a result, the application of the regulation presents manufacturers and suppliers with the challenge of demonstrating compliance. At process level the implementation of a Cyber Security Management System (CSMS) is required while at product level, the Threat Assessment and Risk Analysis (TARA) forms the basis to identify relevant threats and corresponding mitigation strategies. Overall, an issued type approval is internationally recognized by the member states of the UN 1958 Agreement. International recognition implies that uniform assessment criteria are applied to demonstrate compliance and to decide whether security efforts are sufficient.

In vehicle ego-motion estimation, vehicle control, and advanced driver assist systems the vehicle dynamics are described by a few key parameters. The side-slip gradient, being one of them, is used to model the lateral behavior of the vehicle. This parameter is rarely known precisely, since it depends on the vehicle’s mass distribution, its tires, and even the chassis setup. Thus, an online-estimation of the side-slip gradient is beneficial, especially in serial applications. Estimating the side-slip gradient with conventional vehicle sensors such as wheel-speed, steering, and inertial sensors poses a significant challenge since considerable dynamic excitation of the vehicle is required, which is uncommon in normal driving. Here, radar sensors open new opportunities in the estimation of such vehicle dynamics parameters since they allow for an instantaneous measurement of the lateral velocity.

An essential component in the approval of advanced driver assistance systems (ADAS) and automated driving systems (ADS) is the quantification of residual risk, which demonstrates that hazardous behavior (HB) occurs less frequently than specified by a corresponding acceptance criterion. In the case of HB with high potential impact severity, only very low accepted frequencies of occurrence are tolerated. To avoid uncertainties due to abstractions and simplifications in simulations, the proof of the residual risk in systems such as advanced emergency braking systems (AEBS) is often partially or entirely implemented as system-level field test. However, the low rates and high confidence required, common for residual risk demonstrations, result in a significant disadvantage of these field tests: the long driving distance required.

Autonomous Emergency Braking (AEB) systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. Advancements in sensor technology and deep learning have improved vehicle perception and real-world understanding. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to estimate the road conditions. However, AEB systems face notable challenges in urban environments, influenced by complex scenarios and adverse weather conditions such as rain and fog. Therefore, investigating the error tolerance of these estimations is essential for the performance of AEB systems.

Investigating human driver behavior enhances the acceptance of the autonomous driving and increases road safety in heterogeneous environments with human-operated and autonomous vehicles. The previously established driver fingerprint model, focuses on the classification of driving style based on CAN bus signals. However, driving styles are inherently complex and influenced by multiple factors, including changing driving environments and driver states. To comprehensively create a driver profile, an in-car measurement system based on the Driver-Driven vehicle-Driving environment (3D) framework is developed. The measurement system records emotional and physiological signals from the driver, including ECG signal and heart rate. A Raspberry Pi camera is utilized on the dashboard to capture the driver's facial expressions and a trained convolutional neural network (CNN) recognizes emotion. To conduct unobtrusive ECG measurements, an ECG sensor is integrated into the steering wheel.

In pursuit of safety validation of automated driving functions, efforts are being made to accompany real world test drives by test drives in virtual environments. To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing. The signal propagation is modeled by a ray tracing approach simulating the interaction of light waves with the environment.

As part of the safety validation of advanced driver assistance systems (ADAS) and automated driving (AD) functions, it is necessary to demonstrate that the frequency at which the system exhibits hazardous behavior (HB) in the field is below an acceptable threshold. This is typically tested by observation of the system behavior in a field operational test (FOT). For situations in which the system under test (SUT) actively intervenes in the dynamic driving behavior of the vehicle, it is assessed whether the SUT exhibits HB. Since the accepted threshold values are generally small, the amount of data required for this strategy is usually very large. This publication proposes an approach to reduce the amount of data required for the evaluation of emergency intervention systems with a state machine based intervention logic by including the time periods between intervention events in the validation process.

The development of an effective Acoustic Vehicle Alert System (AVAS) is not solely about adhering to safety regulations; it also involves crafting an auditory experience that aligns with the expectations of vulnerable road users. To achieve this, a deep understanding of the acoustic transfer function is essential, as it defines the relationship between the sound emitter (the speaker inside the vehicle) and the receiver (the vulnerable road user). Maintaining the constancy of this acoustic transfer function is paramount, as it ensures that the sound emitted by the vehicle aligns with the intended safety cues and brand identity that is defined by the car manufacturer. In this research paper, three distinct methodologies for calculating the acoustic transfer function are presented: the classical Boundary Element method, the H-Matrix BEM accelerated method, and the Ray tracing method.

Uncrewed Aerial vehicles are useful for a multitude of applications in today’s age, covering a wide variety of fields such as defense, environmental science, meteorology, emergency responders, search and rescue operations, entertainment robotics, etc. Different types of aircrafts such as fixed wing UAVs, rotor wing UAVs are used for the mentioned applications depending upon the application requirements. One such category of UAVs is the lighter-than-air aircrafts, that provide their own set of advantages over the other types of UAVs. Blimps are among the participants of the lighter-than-air category that are expected to offer advantages such as higher endurance and range, and safer and more comfortable Human-machine-Interaction, etc. as compared to fixed wing and rotor wing UAVs due to their design. A ROS (Robot Operating System) based control system was developed for controlling the blimp.

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.

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.

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.

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 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.

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.

With the current trend of including the evaluation of the risk of brain injuries in vehicle crashes due to rotational kinematics of the head, two injury criteria have been introduced since 2013 – BrIC and DAMAGE. BrIC was developed by NHTSA in 2013 and was suggested for inclusion in the US NCAP for frontal and side crashes. DAMAGE has been developed by UVa under the sponsorship of JAMA and JARI and has been accepted tentatively by the EuroNCAP. Although BrIC in US crash testing is known and reported, DAMAGE in tests of the US fleet is relatively unknown. The current paper will report on DAMAGE in NCAP-like tests and potential future frontal crash tests involving substantial rotation about the three axes of occupant heads. Distribution of DAMAGE of three-point belted occupants without airbags will also be discussed. Prediction of brain injury risks from the tests have been compared to the risks in the real world.

The analysis presented in this document demonstrates the mathematical model approach for determining the rotation of a door about the hinge axis. Additional results from the model are the torque due to gravity about the axis, opening force, and the door hold open check link force. Vector mechanics, equations of a plane, and parametric equations were utilized to develop this model, which only requires coordinate points as inputs. This model allows for various hinge axis angles and door rotation angles to quickly be analyzed. Vehicle pitch and roll angles may also be input along with door mass to determine the torque about the hinge axis. The vector calculations to determine the moment arm of the door check link and its resulting force are demonstrated for both a standard check link design and an alternate check link design that has the link connected to a slider translated along a shaft.

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 prediction of agents' future trajectory is a crucial task in supporting advanced driver-assistance systems (ADAS) and plays a vital role in ensuring safe decisions for autonomous driving (AD). Currently, prevailing trajectory prediction methods heavily rely on high-definition maps (HD maps) as a source of prior knowledge. While HD maps enhance the accuracy of trajectory prediction by providing information about the surrounding environment, their widespread use is limited due to their high cost and legal restrictions. Furthermore, due to object occlusion, limited field of view, and other factors, the historical trajectory of the target agent is often incomplete This limitation significantly reduces the accuracy of trajectory prediction. Therefore, this paper proposes ETSA-Pred, a mapless trajectory prediction model that incorporates enhanced temporal modeling and spatial self-attention.

The Advanced Driver Assistance System (ADAS) is a comprehensive feature set designed to aid a driver in avoiding or reducing the severity of collisions while operating the vehicle within specified conditions. In General Motors (GM) vehicles, the primary controller for the ADAS is the Active Safety Control Module (ASCM). In the 2013 model year, GM introduced an ASCM utilizing the GM internal nomenclature of External Object Calculation Module (EOCM) in some of their vehicles produced for the North American market. Similar to the Sensing and Diagnostic Module (SDM) utilized in the restraints system, the EOCM3 LC contains an Event Data Recorder (EDR) function to capture and record information surrounding certain ADAS or Supplemental Inflatable Restraint (SIR) events. The ASCM EDR contains information from external object sensors, various chassis and powertrain control modules, and internally calculated data.