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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.

The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.

Contact our Sales Team! The ADAS to Automated Driving Digital Summit will virtually bring together engineering professionals from OEMs, suppliers, technology and corporate and academic research. And now, with no travel required, the digital summit will reach an even broader international audience from North America, Europe, and Asia.

Pedestrian Automatic Emergency Braking (P-AEB) is a technology designed to avoid or reduce the severity of vehicle to pedestrian collisions. This technology is currently assessed and evaluated via EuroNCAP and similar procedures in which a pedestrian test target is crossing the road, walking alongside the road, or stationary in the forward vehicle travel path. While these assessment methods serve the purpose of providing cross-comparison of technology performance in a standardized set of scenarios, there are many scenarios which could occur which are not considered or studied. By identifying and performing non-EuroNCAP, non-standardized scenarios using similar methodology, the robustness of P-AEB systems can be analyzed. These scenarios help identify areas of further development and consideration for future testing programs. Three scenarios were considered as a part of this work: straight line approach, curved path approach, and parking lot testing.

The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed.

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.

Analysis of pedestrian-to-vehicle collisions can be complex due to the nature of the interaction and the physics involved. The scarcity of evidence like video evidence (from CCTV or dashcams), data from the vehicle's ECU, witness accounts, and physical evidence such as tyre marks, complicates the analysis of these incidents. In cases with limited evidence, current forensic methods often rely on prolonged inquiry processes or computationally intensive simulations. Without adequate data, accurately estimating pedestrian kinematics and addressing hit-and-run scenarios becomes challenging. This research provides an alternative approach to enhancing pedestrian forensic analysis based on machine learning (ML) algorithms trained on over 3000 multi-body computer simulations with a diverse set of vehicle profiles and pedestrian anthropometries.

A total of 93 tests were conducted in daytime conditions to evaluate the effect on the Time to Collision (TTC), emergency braking, and avoidance rates of the Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) provided by a 2022 Tesla Model 3 against a 4ActivePA adult static pedestrian target. Variables that were evaluated included the vehicle speed on approach, pedestrian offsets, pedestrian clothing, and user-selected FCW settings. As a part of the Tesla’s Collision Avoidance AssistTM, these user-selected FCW settings change the timing of the issuance of the visual and/or audible warning provided. This testing evaluated the Tesla at speeds of 25 and 35 miles per hour (mph) versus a stationary pedestrian target in early, medium, and late FCW settings. Testing was also conducted with a 50% pedestrian offset and 75% offset conditions relative to the right side of the Tesla.

Road accidents are a major concern worldwide and vulnerable road users make up more than half of the victims of road accident deaths. In order to combat this issue, several countries worldwide have mandated pedestrian safety test regulations viz., AIS100 & UN-R127. One of the requirements of the regulations is when Flexible Pedestrian Legform Impactor (Flex-PLI) is impacted onto the frontal structure of the vehicle at a speed of 40kmph, the Bending moment (BM) of tibia bone of Flex-PLI shall not exceed the regulatory limit of 340Nm. In this paper, we have built a statistical model for predicting the BM of tibia in Flex-PLI using regression analysis. 13 vehicles have been selected from all applicable vehicle categories viz., Sedan, hatchback, Coupe & SUV/MUV for this undertaking. An exhaustive analysis of the vehicle frontal structures and Flex-PLI test videos have been done to identify & measure the design parameters to be used as predictor variables.

Ensuring the safety of vulnerable road users (VRUs) such as pedestrians, users of micro-mobility vehicles, and cyclists is imperative for the commercialization of automated vehicles (AVs) in urban traffic scenarios. City traffic intersections are of particular concern due to the precarious situations VRUs often encounter when navigating these locations, primarily because of the unpredictable nature of urban traffic. Earlier work from the Institute of Automated Vehicles (IAM) has developed and evaluated Driving Assessment (DA) metrics for analyzing car following scenarios. In this work, we extend those evaluations to an urban traffic intersection testbed located in downtown Tempe, Arizona. A multimodal infrastructure sensor setup, comprising a high-density, 128-channel LiDAR and a 720p RGB camera, was employed to collect data during the dusk period, with the objective of capturing data during the transition from daylight to night.

Automated driving systems (ADS) are designed toward safely navigating the roadway environment, which also includes consideration of potential conflict with other road users. Of particular concern is understanding the cumulative risk associated with vulnerable road users (VRUs) conflicts and collisions. VRUs represent a population of road users that have limited protection compared to vehicle occupants. These severity distributions are particularly useful in evaluating ADS real-world performance with respect to the existing fleet of vehicles. The objective of this study was to present event severity distributions associated with vehicle-cyclist collisions within an urban naturalistic driving environment by leveraging data from third-party vehicles instrumented with forward-facing cameras and a sensor suite (accelerometer sampling at 20 Hz and GPS [variable sampling frequency]). From over 66 million miles of driving, 30 collision events were identified.

Background: The Indian automobile industry, including the auto component industry, is a significant part of the country’s economy and has experienced growth over the years. India is now the world’s 3rd largest passenger car market and the world’s second-largest two-wheeler market. Along with the boon, the bane of road accident fatalities is also a reality that needs urgent attention, as per a study titled ‘Estimation of Socio-Economic Loss due to Road Traffic Accidents in India’, the socio-economic loss due to road accidents is estimated to be around 0.55% to 1.35% of India’s GDP [27] Ministry of road transport and highways (MoRTH) accident data shows that the total number of fatalities on the road are the highest (in number terms) in the world. Though passenger car occupant fatalities have decreased over the years, the fatalities of vulnerable road users are showing an increasing trend. India has committed to reduce road fatalities by 50% by 2030.

The active sound generation systems (ASGS) for electric vehicles (EVs) play an important role in improving sound perception and transmission in the car, and can meet the needs of different user groups for driving and riding experiences. The active sound synthesis algorithm is the core part of ASGS. This paper uses an efficient variable-range fast linear interpolation method to design a frequency-shifted and pitch-modified sound synthesis algorithm. By obtaining the operating parameters of EVs, such as vehicle speed, motor speed, pedal opening, etc., the original sound signal is interpolated to varying degrees to change the frequency of the sound signal, and then the amplitude of the sound signal is determined according to different driving states. This simulates an effect similar to the sound of a traditional car engine. Then, a dynamic superposition strategy is proposed based on the Hann window function.

Abstract Compared to other age groups, older adults are at more significant risk of hip fracture when they fall. In addition to the higher risk of falls for the elderly, fear of falls can reduce this population’s outdoor activity. Various preventive solutions have been proposed to reduce the risk of hip fractures ranging from wearable hip protectors to indoor flooring systems. A previously developed rubberized asphalt mixture demonstrated the potential to reduce the risk of head injury. In the current study, the capability of the rubberized asphalt sample was evaluated for the risk of hip fracture for an average elderly male and an average elderly female. A previously developed human body model was positioned in a fall configuration that would give the highest impact forces toward regular asphalt.

To harmonize and define terminology associated with occupant protection for children for vehicle manufacturers and child restraint manufacturers in the United States and Canada.

Many Vulnerable Road Users (VRUs) experience injuries and fatalities every year, making road safety a challenge in the World. According to the Ministry of Road Transport and Highway (MoRTH) during the year 2021, a total number of 4,12,432 road accidents have been reported in India, claiming 1,53,972 lives and causing injuries to 3,84,448 persons. In terms of road-user categories, the total fatality of the Pedestrian road-users was 18.9 per cent of persons killed in road accidents. One of the ways to handle the situation is to protect pedestrians utilizing active safety measures in the vehicle. In addition, active safety research heavily relies on perceptions of pre-crash scenarios. The objective of the study is to examine passenger car-to-pedestrian scenarios and model active safety system in a car to prevent or mitigate collisions.

The commercial vehicle sector (especially trucks) has major role in economic growth of a nation. With improving infrastructure, increasing number of commercial vehicles and growing amount of Vulnerable Road Users (VRUs) on roads, accidents are also increasing. As per RASSI (Road Accident Sampling System India) FY2016-21 database, commercial vehicles are involved in 43% of total accidents on Indian roads. One of the major causes of these accidents is Driver Drowsiness and Inattention (DDI) (approx. 10% contribution in total accidents). This paper describes novel driver-in-loop performance assessment methodology for comprehensive verification of Driver Monitoring System (DMS) for commercial vehicle application. Novelty lies in specification of test subjects, driving styles and variety of road traffic scenarios for verification of DMS system. Test setup is made modular to cater to different platform environments (Heavy, Intermediate, Light) with minor modifications.

Designing an effective AVAS system, not only to meet safety regulations, but also to create the expected perception for the vulnerable road user, relies on knowledge of the acoustic transfer function between the sound actuator and the receiver. It is preferable that the acoustic transfer function be as constant as possible to allow transferring the sound designed by the car OEM to ensure the safety of vulnerable road users while conveying the proper brand image. In this paper three different methodologies for the acoustic transfer function calculations are presented and compared in terms of accuracy and calculation time: classic Boundary Element method, H-Matrix BEM accelerated method and Ray tracing method. An example of binaural listening experience at different certification positions in the modeled simulated space is also presented.

The car door handle is an essential component of any vehicle, as it plays a crucial role in providing access to the cabin and ensuring safety of the passenger. The primary function of the car door handle is to allow entry and exit from the vehicle while preventing unauthorized access. In addition to this, car door handles also play a critical role in ensuring passenger safety by keeping the door closed during accidents or when there is a significant amount of G-force acting on the vehicle. A typical car door handle comprises several components including the structure, cover, bowden lever, bracket, pins and other child parts. The structure provides the ergonomics and rigidity for grabbing the handle, while the cover gives the handle an aesthetic appearance. The Bowden lever facilitates the unlatching of the door and the intermediate parts ensure that the handle operates smoothly.