As debates about the policy and ethical implications of AI systems grow, it will be increasingly important to accurately locate who is responsible when agency is distributed in a system and control ...over an action is mediated through time and space. Analyzing several high-profile accidents involving complex and automated socio-technical systems and the media coverage that surrounded them, I introduce the concept of a moral crumple zone to describe how responsibility for an action may be misattributed to a human actor who had limited control over the behavior of an automated or autonomous system. Just as the crumple zone in a car is designed to absorb the force of impact in a crash, the human in a highly complex and automated system may become simply a component—accidentally or intentionally—that bears the brunt of the moral and legal responsibilities when the overall system malfunctions. While the crumple zone in a car is meant to protect the human driver, the moral crumple zone protects the integrity of the technological system, at the expense of the nearest human operator. The concept is both a challenge to and an opportunity for the design and regulation of human-robot systems. At stake in articulating moral crumple zones is not only the misattribution of responsibility but also the ways in which new forms of consumer and worker harm may develop in new complex, automated, or purported autonomous technologies.
A widening gap between official and real-world fuel consumption of passenger cars has been reported worldwide. However, previous policy evaluation has not adequately incorporated real-world ...performance. To comprehensively evaluate China’s Corporate Average Fuel Consumption (CAFC) policy, we update the fuel consumption gap with millions of consumer-reported records and calculate CAFC and evaluate the national average fuel consumption (NAFC) under four scenarios. The results show that China’s fuel consumption gap has reached 37%. The average CAFC decreases from 6.72 L/100 km in 2016 to 5.91 L/100 km in 2020, far slower than the rated performance. The real-world NAFC non-compliance is disaggregated into on-road discrepancy (2.5 L/100 km), new-energy discounts (0.4 L/100 km), lightweight impacts (0.3 L/100 km), and additional technology improvements (0.8 L/100 km). This study can improve the state-of-the-art understanding of the real-world fuel consumption of passenger cars in China, thus calling for a more real-world-featured regulation system.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. ...On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.
The paper is devoted to a theoretical study of the car movement stability when exposed to longitudinal forces of a quasistatic nature. The authors obtained the expressions for calculating the ...resistance coefficient of the car lift by the longitudinal compressive force acting on the car as part of a freight train. The influence of some factor's combination on the longitudinal forces, at which the car movement stability is still maintained, was analyzed using the analytical dependencies to assess the longitudinal loading of cars in trains. The study was carried out by analytical method for assessing the freight car stability when moving at different speeds along curved track sections. The calculations were made in a curve of small radius taking into account the inertia forces from the unbalanced acceleration. In a theoretical study, the influence of quasistatic longitudinal compressive forces depending on changes in speed and force value, as well as the influence of friction in the wheel flange and rail contact and the eccentricity of fastening the automatic coupler shank on the stability were considered. When applying the results obtained, the stability of freight rolling stock can be increased, which in turn will remove some existing restrictions on the permissible speeds and increase the technical speed of train movement. Using the described methodology for determining the lift resistance coefficient will make it possible to justify the cause of derailment, as well as to develop and put into practice technical measures to prevent the lift of carriages, thrusts and shears of the track.
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO’s evolution, examining the ...innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with transformers. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO’s development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.
Self-driving vehicles have experienced an increase in research interest in the last decades. Nevertheless, fully autonomous vehicles are still far from being a common means of transport. This paper ...presents the design and experimental validation of a processor-in-the-loop (PIL) architecture for an autonomous sports car. The considered vehicle is an all-wheel drive full-electric single-seater prototype. The retained PIL architecture includes all the modules required for autonomous driving at system level: environment perception, trajectory planning, and control. Specifically, the perception pipeline exploits obstacle detection algorithms based on Artificial Intelligence (AI), and the trajectory planning is based on a modified Rapidly-exploring Random Tree (RRT) algorithm based on Dubins curves, while the vehicle is controlled via a Model Predictive Control (MPC) strategy. The considered PIL layout is implemented firstly using a low-cost card-sized computer for fast code verification purposes. Furthermore, the proposed PIL architecture is compared in terms of performance to an alternative PIL using high-performance real-time target computing machine. Both PIL architectures exploit User Datagram Protocol (UDP) protocol to properly communicate with a personal computer. The latter PIL architecture is validated in real-time using experimental data. Moreover, they are also validated with respect to the general autonomous pipeline that runs in parallel on the personal computer during numerical simulation.
Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial ...Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Following the signing of the JCPOA nuclear deal between Iran and the 5+1 countries in June 2015 and the subsequent lifting of international sanctions, many observers predicted that Chinese carmakers ...would be squeezed out of the Iranian market by European and South Korean manufacturers. In 2018 however, after the Trump administration withdrew the United States from the agreement and reinstated sanctions, the situation moved in favour of the Chinese car industry. China has now become the biggest overseas player in Iran's automobile market. This article argues that the abandonment of the JCPOA by Washington led to the 'China-ization' of the car industry in Iran and shows how Iranian carmakers played an instrumental role in this process.
Full text
Available for:
BFBNIB, NUK, PILJ, SAZU, UL, UM, UPUK
Information technology is based on data management between various sources. Software projects, as varied as simple applications or as complex as self-driving cars, are heavily reliant on the amounts, ...and types, of data ingested by one or more interconnected systems. Data is not only consumed but is transformed or mutated which requires copious amounts of computing resources. One of the most exciting areas of cyber-physical systems, autonomous vehicles, makes heavy use of deep learning and AI to mimic the highly complex actions of a human driver. Attempting to map human behavior (a large and abstract concept) requires large amounts of data, used by AIs to increase their knowledge and better attempt to solve complex problems. This paper outlines a full-fledged solution for managing resources in a multi-cloud environment. The purpose of this API is to accommodate ever-increasing resource requirements by leveraging the multi-cloud and using commercially available tools to scale resources and make systems more resilient while remaining as cloud agnostic as possible. To that effect, the work herein will consist of an architectural breakdown of the resource management API, a low-level description of the implementation and an experiment aimed at proving the feasibility, and applicability of the systems described.
Full text
Available for:
IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
PDF
