Search Results

Abstract Autonomous vehicles (AVs) rely heavily upon their perception subsystems to “see” the environment in which they operate. Unfortunately, the effect of variable weather conditions presents a significant challenge to object detection algorithms, and thus, it is imperative to test the vehicle extensively in all conditions which it may experience. However, the development of robust AV subsystems requires repeatable, controlled testing—while real weather is unpredictable and cannot be scheduled. Real-world testing in adverse conditions is an expensive and time-consuming task, often requiring access to specialist facilities. Simulation is commonly relied upon as a substitute, with increasingly visually realistic representations of the real world being developed.

Abstract Vehicular communications face unique security issues in wireless communications. While new vehicles are equipped with a large set of communication technologies, product life cycles are long and software updates are not widespread. The result is a host of outdated and unpatched technologies being used on the street. This has especially severe security impacts because autonomous vehicles are pushing into the market, which will rely, at least partly, on the integrity of the provided information. We provide an overview of the currently deployed communication systems and their security weaknesses and features to collect and compare widely used security mechanisms. In this survey, we focus on technologies that work in an ad hoc manner. This includes Long-Term Evolution mode 4 (LTE-PC5), Wireless Access in Vehicular Environments (WAVE), Intelligent Transportation Systems at 5 Gigahertz (ITS-G5), and Bluetooth.

Abstract Car manufacturers are continuously improving passenger comfort by advancing technologies including highly automated driving. Before the broad introduction of automated driving, specific human factors regarding passenger comfort must be considered, including motion sickness. Therefore, the identification of the frequency of motion sickness and associated factors in the population is needed to extrapolate the effects for future mobility systems. We conducted three surveys between 2015 and 2020, asking people questions about their experience with motion sickness in cars. Based on the responses of 1165 participants, gender and age showed a strong influence on the self-reported frequency of motion sickness. For deeper analysis, a logistic order regression model was used to estimate the frequency of motion sickness for different user typologies.

Abstract The use of appropriate loads and regulations is of great importance in weld fatigue assessment of rail on-track maintenance equipment and similar vehicles for optimized design. The regulations and available loads, however, are often generalized for several categories, which proves to be overly conservative for some specific categories of machines. EN (European Norm) and AAR (Association of American Railroads) regulations play a pivotal role in determining the applicable loads and acceptance criteria within this study. The availability of track-induced fatigue load data for the cumulative damage approach in track maintenance machines is often limited. Consequently, the FEA-based validation of rail track maintenance equipment often resorts to the infinite life approach rather than cumulative damage approach for track-induced travel loads, resulting in overly conservative designs.

Abstract The first objective of this study, addressed in Part 1, is to use finite element (FE) human body modeling (HBM) to evaluate the tangent of the Belt-to-Pelvis angle (tanθBTP) as a submarining predictor in frontal crashes for occupants in reclined seats. The second objective, addressed in Part 2, is to use this predictor to assess two technical solutions for reducing submarining risks for two different occupant anthropometries. In Part 1, tanθBTP (the lap belt penetration from the anterior superior iliac spine [ASIS] in the abdominal direction) was evaluated in impact simulations with varying seat belt anchor positions. Sled simulations with a 56 km/h full-frontal crash pulse were performed with the SAFER HBM morphed to the anthropometry of a small female and average male. A correlation was found between the submarining predictor and submarining.

Abstract The automotive industry offers many applications for machine learning (ML), in general, and deep neural networks in particular. However, the real-world deployment of neural networks into safety-critical components remains a challenge as models would need to offer robustness under a wide range of operating conditions. In this work, we focus on uncertainty estimation, which can be used to deliver predictors that fail gracefully, by detecting situations where their predictions are unreliable. Following Gräber et al. [1], we use Recurrent Neural Networks (RNNs) to perform sideslip angle estimation. To perform robust uncertainty estimation, we augment the RNNs with generative models. We demonstrate the advantage of the proposed model architecture over Monte Carlo (MC) dropout [2] on the Revs data set [3].

Abstract Sustainable practices are taking precedence across many industries, as evident from their shift towards the use of environmentally responsible materials, such as natural fiber-reinforced acrylated epoxidized soybean oil (NF-AESO). However, due to the lower reactivity of AESO, the curing reaction usually requires higher temperatures and longer curing time (e.g., 150°C for 6-12 h), thus making the entire process unsustainable. In this study, we demonstrate the potential power of photons towards manufacturing NF-AESO composites in a sustainable manner at room temperature (RT) within 10 min. Two photoinitiators, i.e., the 2,2-dimethoxy phenylacetophenone (DMPA) and 1-hydroxycyclohexyl phenyl ketone (HCPK), were evaluated and compared with the thermal initiator, i.e., tert-butyl perbenzoate (TBPB). Based on the mechanical performance of the AESOs, the photoinitiation system for NF-AESO was optimized.

Abstract The external driving environment of an autonomous driving vehicle is complex and changeable. In this article, the trajectory tracking control with obstacle avoidance based on model predictive control was presented. Specifically, double-level control scheme by controlling the front steering angle was used in our research, and the double level is composed of the high level of model predictive controller for local trajectory planning and low level of model predictive controller for trajectory tracking. At high level, the local trajectory planner based on the point-mass model was designed. Then, at low level, the linear time-varying vehicle dynamics model was presented, and the trajectory tracking controller was proposed considering control variable, control increment, and output constraint. Finally, the trajectory tracking performance was tested in co-simulation environment with CarSim and Simulink, and the tracking errors were analyzed.

Abstract Recently, high-resolution sensors capable of multiple detections (MDs) per object are available for perception applications in autonomous or semi-autonomous vehicles. Conventional multi-target tracking (MTT) approaches start with the point-target assumption and thus cannot be applied directly to the MDs of high-resolution sensors. A popular solution widely used in literature starts with a measurement partitioning approach, followed by repurposing conventional tracking algorithms to accommodate the resulting partitions. However, the computational requirement increases combinatorially, especially under multi-sensor applications that also independently return multiple radar reflections as in the automotive radar sensors used in this work. Thus, a hybrid approach that combines a clustering technique (such as DBSCAN) to alleviate the computational complexity and an MD tracking scheme that admits multiplicity of the target detections is employed.

Abstract The promise and potential for a future of automated vehicles (AVs) remains great, with safety and societal transformations that may rival the original introduction of the automobile. Yet an inability for industry and governments to define what it means for an AV to drive safely has tempered enthusiasm and risks causing a “winter of AV” just like the one that affected Artificial Intelligence technologies decades ago, which is only now being overcome. Towards this end, the Responsibility-Sensitive Safety (RSS) model was introduced as an open and transparent white-box, an interpretable and scalable formal model that defines minimum safety requirements based on reasonable assumptions of others, balancing safety and usefulness for automated driving vehicles.

Abstract Traditionally, Electronic Control Units (ECUs) in vehicles have been left unsecured. Ensuring cybersecurity in an ECU network is challenging as there is no centralized authority in the vehicle to provide security as a service. While progress has been made to address cybersecurity vulnerabilities, many of these approaches have focused on enterprise, software-centric systems and require more computational resources than typically available for onboard vehicular devices. Furthermore, vehicle networks have the additional challenge of mitigating security vulnerabilities while satisfying safety and performance constraints. This article introduces a blockchain framework to detect unauthorized modifications to vehicle ECUs. A proof of concept blockchain prototype framework is implemented on a set of microprocessors (comparable to those used by simple ECUs) as a means to assess the efficacy of using our blockchain approach to detect unauthorized updates.

Abstract This article presents a new machine learning (ML) development lifecycle which will constitute the core of the new aeronautical standard on ML called AS6983, jointly being developed by working group WG-114/G34 of EUROCAE and SAE. The article also presents a survey of several existing standards and guidelines related to ML in aeronautics, automotive, and industrial domains by comparing and contrasting their scope, purpose, and results.

Abstract Scenario-based testing is a promising approach to solving the challenge of proving the safe behavior of vehicles equipped with automated driving systems (ADS). Since an infinite number of concrete scenarios can theoretically occur in real-world road traffic, the extraction of scenarios relevant in terms of the safety-related behavior of these systems is a key aspect for their successful verification and validation. Therefore, a method for extracting multimodal urban traffic scenarios from naturalistic road traffic data in an unsupervised manner, minimizing the amount of (potentially biased) prior expert knowledge, is proposed. Rather than an (elaborate) rule-based assignment by extracting concrete scenarios into predefined functional scenarios, the presented method deploys an unsupervised machine learning pipeline. The approach allows for exploring the unknown nature of the data and their interpretation as test scenarios that experts could not have anticipated.

Abstract Distributed drive electric vehicles can apply the four-wheel differential drive to change the vehicle handling performance, which can make the connected and automated vehicles (CAV) more controllable. This article proposes a hierarchical scheme of the torque-vectoring controller (TVC), whose key parameters affecting the control objective are optimized from the human-vehicle closed-loop simulation test. First, the radial basis function (RBF)-based adaptive second-order sliding mode control (RASOSMC) for additional yaw moment generation is designed in the upper layer of the controller. The lower layer is the torque distribution strategy that takes into consideration the minimization of the tire load and the control error of the additional yaw moment and yaw rate. Afterward, the longitudinal and lateral driver model with the adaptive correction of preview time is established.

Abstract Non-pneumatic tires (NPTs) have been widely used due to their advantages of no occurrence of puncture-related problems, no need of air maintenance, low rolling resistance, and improvement of passenger comfort due to its better shock absorption. It has a variety of applications as in earthmovers, planetary rover, stair-climbing vehicles, and the like. Recently, the unique puncture-proof tire system (UPTIS) NPT has been introduced for passenger vehicles segment. The spoke design of NPT-UPTIS has a significant effect on the overall working performance of tire. Optimized tire performance is a crucial factor for consumers and original equipment manufacturers (OEMs). Hence to optimize the spoke design of NPT-UPTIS spoke, the top and bottom curve of spoke profile have been described in the form of analytical equations. A generative design concept has been introduced to create around 50,000 spoke profiles.

Abstract Multi-agent autonomous racing still remains a largely unsolved research challenge. The high-speed and close proximity situations that arise in multi-agent autonomous racing present an ideal condition to design algorithms which trade off aggressive overtaking maneuvers and minimize the risk of collision with the opponent. In this article we study a two-vehicle autonomous racing setup and present AutoPass—a novel framework for overtaking in a multi-agent setting. AutoPass uses the structure of an automaton to break down the complex task of overtaking into sub-maneuvers that balance overtaking likelihood and risk with safety of the ego vehicle. We present real-world implementation of 1/10-scale autonomous racing cars to demonstrate the effectiveness of AutoPass for the overtaking task.

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract To protect ship equipment of river and sea transport, it is suggested to use polymeric protective coatings based on epoxy diane oligomer ED-20, polyethylene polyamine (PEPA) curing agent and filler, which is a departure from industrial production. Thus the purpose of the work is analysis of major dependency of the properties on the content of fillers that allowed to revealed the critical filler content (furnace black) in composites to form a protective coating with the required set of characteristics. The infrared (IR) spectral analysis was used to investigate the presence of bonds on the surface of particles of the PM-75 furnace black, which allows us to assess the degree of cross-linking of the polymer. The influence of the content of dispersed furnace black on the physicomechanical and thermophysical properties and the structure of the protective coating is investigated.

Abstract The causes of failure due to cracking in the resistance spot welding of the advanced high-strength steels dual-phase 590 (DP590) were investigated using scanning electron microscopy (SEM), optical microscopy, and the tensile-shear test. The results showed that by increasing the current amount, the formation of the melting zone occurred in the heat-affected zone, leading to the cracking in this area, reducing the tensile strength and decreasing the mechanical properties; the initiation and growth of cracking and failure in this region also happened. In the heat-affected zone, by increasing the current amount with the softening phenomenon, the recrystallized coarse grains also occurred, eventually resulting in the loss of mechanical properties. The results of the tensile-shear test also indicated that by increasing the current up to 12 kA, the strength was raised, but the ductility was reduced.

Abstract Heavy commercial vehicles play an important role in creating the trade and economic balance of countries. Also, the durability and safety of heavy commercial vehicles come to the fore. Heavy commercial vehicles consist of two parts. These are the chassis area with the equipment that allows the vehicle to move and the cabin section where the driver is located. The cabin area is the most important area that ensures the highest level of driver safety. Considering that the production of trucks is increasing day by day, it is inevitable for companies to increase their R&D activities in the field of cabin and cabin suspension systems for much safer, durable, and comfortable trucks. This study aims to determine the safe torque value of the fasteners and their assembly sequence of the Cab Suspension Console, which is one of the most important connection parts in a truck and which can cause a fatal accident by breaking.