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![Figure 2 Functional component classification according to SAE J3016 [3].](publication/339371847/figure/fig2/AS:860500191637504@1582170641026/Functional-component-classification-according-to-SAE-J3016-3_Q320.jpg)
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The expansion of electric vehicles in urban areas has paved the way toward the era of autonomous vehicles, improving the performance in smart cities and upgrading related driving problems. This field of research opens immediate applications in the tourism areas, airports or business centres by greatly improving transport efficiency and reducing rep...
Autonomous driving faces the difficulty of securing the lowest possible software execution times to allow a safe and reliable application. One critical variable for autonomous vehicles is the latency from the detection of obstacles to the final actuation response of the vehicle, especially in the case of high-speed driving. A prerequisite for auton...
In autonomous driving, there has been an explosion in the use of deep neural networks for perception, prediction and planning tasks. As autonomous vehicles (AVs) move closer to production, multi-modal sensor inputs and heterogeneous vehicle fleets with different sets of sensor platforms are becoming increasingly common in the industry. However, neu...
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... Vehicles* * (Serban, Poll, & Visser, 2020) ADAS includes a range of features, including adaptive cruise control (ACC), lane keep assist (LKA), forward emergency braking (FEB), etc. While the intention may be to improve passenger and pedestrian safety and enhance the driving experience, the driver must assimilate a lot of new information to properly use the advanced features. ...
Adaptive cruise control, lane keep assist, and forward emergency braking are some examples of advanced driver assistance systems (ADAS) currently available in many automotive vehicles on the road today. As the capabilities of ADAS in vehicles continue to grow, is the driver’s understanding of these advanced features and their limitations also growing? We administered a 15-item online survey gauging familiarity, perceived importance, comfort using, and learning resource preferences of ADAS to 956 participants. These participants were drivers with experience in owning or renting ADAS-equipped vehicles. In brief, findings indicated that drivers perceive ADAS as important, but lacked an understanding of key safety precautions for optimal use and preferred non-corrective features. Based on the results of this study, future research should consider a method of consolidating learning to increase user knowledge and use of ADAS in vehicles.
... There is a concern for designing functional software components which are deployed on ECU and are required to exchange information to be proccessed astutely, using AI algorithms. [8] Another example are sensor abstractions that provide software interfaces to hardware sensors, possible adapters and conversion functionality needed to interpret the data. ...
... Proposed functional architecture of Autonomous Vehicles Components[8] The architecture of smart city with IoT ...
Recent advancements and technological breakouts in driving assist have fortified the concept of autonomous driving cars. Latest market demands are foreshadowing the evolution of safer and more reliable cars and as we approach new engineering capabilities,human interference will be limited from decreased to none. Although there is a variety of AI software architectures, vehicle localization techniques and telecommunications in literature, there is a lack of effort in comparing these techniques and identifying their potentials and limitations for autonomous vehicle applications used in modern smart cities. In the inception of the modern era, cities encounter new challenges, hence they are called to be "smarter". Fault-tolerant, and interoperable technologies, innovative Smart City solutions and IoT infrastructures must coexist in order to make the ability of intercommunicating cars a fully functional concept [1]. This paper continues the analysis with IoT applications and advancements in transportation system architectures required for Smart Cities. IoT refers to practically any device (e.g. cellphone, laptops, TVs) that can and will connect to the internet without necessarily having a processor unit. The idea of Smart Cities was introduced to make traffic control, everyday driving experience, emergency situation-handling automated and smart, meaning there are ways for the machine (e.g. the car) to learn and avoid possible errors.
... When it comes to road vehicles, there are a handful of Levels of Automation (LOA) models that are used as a means to classify the maturity of automation technology in contemporary vehicles, as well as providing a roadmap towards full vehicle autonomy. Probably the most commonly used LOA model is that defined by the Society of Automotive Engineers (SAE), shown in Figure 2 (Serban et al., 2020). The SAE LOA model was originally intended to provide clarification to automation designers and engineers, but it is problematic in how it is used today for several reasons. ...
The discussion of ethics in relation to AI often centres on concerns about privacy, surveillance, bias, discrimination and attempts to use AI to influence human behaviour. When considering ethical AI in transportation, the "trolley problem" dilemma is often the issue that immediately comes to mind, leading to discussions about whether and how autonomous vehicles can or should make moral judgements about safety in extreme situations. In this paper, I present an exploration of a different kind of ethical discussion in relation to autonomous vehicles, asking the question about whether it is ethical to allow the deployment of so-called "self-driving" cars on the roads when there is overwhelming evidence of the lack of maturity of the technology, and the risk perception of its users. I consider the topics of "informed consent" and "responsible deployment" in autonomous vehicle technology development and present examples that illustrate why these are important issues that must be addressed. Finally, I present suggestions of how the ethical question could be addressed through greater transparency and openness about the capabilities and limitation of autonomous vehicle technology.
... The i-CAVE research project uses the multi-layered functional architecture, as proposed in [18,19]. This architecture has multiple layers through which information flows from the left to right, from sensors to actuators, decisions at each layer level. ...
... Fig. 2 shows the layers and components that are used in an automated vehicle functional architecture. Refer to [18,19], and [20], for more details. ...
Cooperative automated vehicle software needs to use advanced software engineering design methodologies to manage the complexity of distributed software. This article presents a design model of a stereo vision system for cooperative automated vehicles based on object-oriented analysis and design methodologies and unified modeling language concepts. We use a rational unified process and the 4+1 architectural view model to design the stereo vision system. The designed model is a part of the perception system of the ego vehicle. The stereo vision system continuously perceives the traffic environment using an on-board front-facing stereo camera and sends information to the vehicle control system. The stereo vision system is developed on Ubuntu 16.04 LTS platform with the Nvidia DriveWorks framework using the C++ programming language, and it is deployed on Nvidia Drive PX2 automotive-grade embedded hardware platform. The designed and developed system is evaluated on the Carla simulation environment and the Renault Twizy cooperative automated vehicle research platform. The experimental results show that the approach is applicable, and the system is capable of running in real-time.
... In addition to the impairing influence of drowsiness in manual driving, monitoring the drivers' state and their ability to control the car is one of the main requirements of the third level of automated vehicles [5]. At this automated level, the driver will still be responsible for the car's performance and they should act safely and promptly in case of automated system faults or complex traffic scenarios [6,7]. Notwithstanding research findings [8] showing that learning can interact with fatigue, leading to shorter reaction times, most previous studies demonstrated that drowsiness has a significant influence to increase drivers' reaction time for braking or steering maneuvers in risky situations to prevent accidents [9][10][11]. ...
... The slower reaction time in the automated mode as compared to manual mode needs to be considered in the design of the human-machine interaction for the third level of automated vehicles [5]. At level three, the driver is expected to act safely and promptly in case of automated system faults or complex traffic scenarios [6,7]. For this, a longer handover time is recommended. ...
... Our results show that a slower reaction time needs to be accounted for in developing systems for the third level of automation. At this level, the driver is expected to take over the control of the vehicle in complex traffic scenarios or cases of automation failures [6,7]. In addition, the results of this study show that drivers' age and condition (e.g., fatigue) are variables that will also need consideration in the development of adaptive automation. ...
This study explores how drivers are affected by automation when driving in rested and fatigued conditions. Eighty-nine drivers (45 females, 44 males) aged between 20 and 85 years attended driving experiments on separate days, once in a rested and once in a fatigued condition, in a counterbalanced order. The results show an overall effect of automation to significantly reduce drivers’ workload and effort. The automation had different effects, depending on the drivers’ conditions. Differences between the manual and automated mode were larger for the perceived time pressure and effort in the fatigued condition as compared to the rested condition. Frustration was higher during manual driving when fatigued, but also higher during automated driving when rested. Subjective fatigue and the percentage of eye closure (PERCLOS) were higher in the automated mode compared to manual driving mode. PERCLOS differences between the automated and manual mode were higher in the fatigued condition than in the rested condition. There was a significant interaction effect of age and automation on drivers’ PERCLOS. These results are important for the development of driver-centered automation because they show different benefits for drivers of different ages, depending on their condition (fatigued or rested).
... Functional architectures for autonomous vehicles have been studied [13][14][15][16][17]. However, literatures hardly identify relationships between software components for the overall automotive system. ...
... Throughout the paper we use an example from autonomous driving, inspired by [1,10] -the design of a perception system for scene understanding. The system performs three tasks: (1) object detection, which aims to identify the location of all objects in an image, (2) semantic segmentation, which assigns each pixel in an image to a predefined class, and (3) depth estimation, which determines the position of obstacles or the road surface. ...
... In Figure 1a and Figure 1b we present two architecture candidates inspired by [10] and [1]. The relevant functional components are illustrated using circles while the input coming from the camera is depicted with a rectangle. ...
... The first figure illustrates the end-to-end paradigm, where all components of the system are jointly trained to form a representation relevant to planning. This corresponds to the recommendation in [10]. The components share a base network for feature extraction and have independent layers to decode the features for each task. ...
The adoption of machine learning (ML) components in software systems raises new engineering challenges. In particular, the inherent uncertainty regarding functional suitability and the operation environment makes architecture evaluation and trade-off analysis difficult. We propose a software architecture evaluation method called Modeling Uncertainty During Design (MUDD) that explicitly models the uncertainty associated to ML components and evaluates how it propagates through a system. The method supports reasoning over how architectural patterns can mitigate uncertainty and enables comparison of different architectures focused on the interplay between ML and classical software components. While our approach is domain-agnostic and suitable for any system where uncertainty plays a central role, we demonstrate our approach using as example a perception system for autonomous driving.
Harm can be caused to people and property by any highly-automated system, even with a human user, due to misuse or design; but which human has the legal liability for the consequences of the harm is not clear, or even which laws apply. The position is less clear for an interdependent Autonomous Human Machine Team System (A-HMT-S) which achieves its aim by reallocating tasks and resources between the human Team Leader and the Cyber Physical System (CPS). A-HMT-S are now feasible and may be the only solution for complex problems. However, legal authorities presume that humans are ultimately responsible for the actions of any automated system, including ones using Artificial Intelligence (AI) to replace human judgement. The concept of trust for an A-HMT-S using AI is examined in this paper with three critical questions being posed which must be addressed before an A-HMT-S can be trusted. A hierarchical system architecture is used to answer these questions, combined with a method to limit a node’s behaviour, ensuring actions requiring human judgement are referred to the user. The underpinning issues requiring Research and Development (R&D) for A-HMT-S applications are identified and where legal input is required to minimize financial and legal risk for all stakeholders. This work takes a step towards addressing the problems of developing autonomy for interdependent human-machine teams and systems.
