Browse Topic: Safety testing and procedures
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a
Performing highly representative tests of aircraft equipment is a critical feature for gaining utmost confidence on their ability to perform flawlessly in flight under the entire spectrum of operating conditions. This can also contribute to accelerate the certification process of a new equipment. A research project (E-LISA) was performed in recent years, as part of the European funded Clean Sky 2 framework, with the objective of building an innovative facility for testing an electrically actuated landing gear and brake for a small air transport. The project eventually led to the development and construction of an Iron Bird able to reproduce in a realistic and comprehensive way a full variety of landing test cases consistent with certification specifications and landing histories available in the repository of the airframer. The Iron Bird that was eventually developed is a multi-functional intelligent and easy reconfigurable facility integrating hardware and software allowing to perform
Airworthiness certification of aircraft requires an Airworthiness Security Process (AWSP) to ensure safe operation under potential unauthorized interactions, particularly in the context of growing cyber threats. Regulatory authorities mandate the consideration of Intentional Unauthorized Electronic Interactions (IUEI) in the development of aircraft, airborne software, and equipment. As the industry increasingly adopts Model-Based Systems Engineering (MBSE) to accelerate development, we aim to enhance this effort by focusing on security scope definitions – a critical step within the AWSP for security risk assessment that establishes the boundaries and extent of security measures. However, our findings indicate that, despite the increasing use of model-based tools in development, these security scope definitions often remain either document-based or, when modeled, are presented at overly abstract levels, both of which limit their utility. Furthermore, we found that these definitions
A total of 148 tests were conducted to evaluate the Forward Collision Warning (FCW) and Automatic Emergency Braking (AEB) systems in five different Tesla Model 3 vehicles between model years 2018 and 2020 across four calendar years. These tests involved stationary vehicle targets, including a foam Stationary Vehicle Target (SVT), a Deformable Stationary Vehicle Target (DSVT), a live vehicle with brake lights, and a SoftCar360 designed for high-speed impact tests. The evaluations were conducted at speeds of 35, 50, 60, 65, 70, 75, and 80 miles per hour (mph) during both daytime and nighttime conditions and utilized early and medium FCW settings. These settings, part of Tesla's Collision Avoidance AssistTM, modify object detection alerts and the timing of visual and auditory warnings issued to drivers. The 2018 to 2020 vehicles initially utilized cameras, radar and ultrasonic sensors (USS) for object detection. Tesla updated their Autoilot software and detection algorithms to a vision
As the high-quality development of the new energy vehicle (NEV) and traction battery industries, the safety of traction batteries has become a global focus. Typically mounted at the bottom of NEVs, traction battery systems are particularly vulnerable to mechanical damage caused by bottom impacts, posing serious safety risks. This study investigates the damage sustained by NEV traction battery systems during bottom impact collisions, using computer tomography analysis to detail the damage mechanisms. The findings provide valuable data to enhance the safety and protective performance of traction batteries under such scenarios.
Headlight glare remains a persistent problem to the U.S. driving public. Over the past 30 years, vehicle forward lighting and signaling systems have evolved dramatically in terms of styling and lighting technologies used. Importantly, vehicles driven in the U.S. have increased in size during this time as the proportion of pickup trucks and sport-utility vehicles (SUVs) has increased relative to passenger sedans and other lower-height vehicles. Accordingly, estimates of typical driver eye height and the height of lighting and signaling equipment on vehicles from one or two decades ago are unlikely to represent the characteristics of current vehicles in the U.S. automotive market. In the present study we surveyed the most popular vehicles sold in the U.S. and carried out evaluations of the heights of lighting and signaling systems, as well as typical driver eye heights based on male and female drivers. These data may be of use to those interested in understanding how exposure to vehicle
Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for reproducing driving scenarios. CARLA allows for both manual control and traffic manager control (the module that controls vehicles in autopilot manner in the simulation). However, current versions of CARLA are limited to setting the start and destination points for vehicles that are controlled by traffic manager, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules are built into the system. This paper presents an extension to CARLA’s source code, enabling the replay of exact vehicle trajectories, irrespective of safety implications
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