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In systems in which many heterogeneous agents operate autonomously, with competing goals and without a centralized planner or global information repository, safety and performance can only be guaranteed by “social” rules imposed on the behavior of ...

Broadening the scope and the sophistication of onboard and outward facing communication is a key enabling technology for autonomous vehicles. With these communication advances come increased automotive cybersecurity vulnerabilities that may be ...

Technology advances give us the hope of driving without human error, reducing vehicle emissions and simplifying an everyday task with the future of self-driving cars. Making sure these vehicles are safe is very important to the continuation of ...

The article provides an overview of the current situation of interaction between self-driving cars and a smart city. We will look at what successes have been achieved in the adaptation of autonomous vehicle in the context of smart urban mobility. ...

Deep Neural Networks (DNNs) have been widely used to perform real-world tasks in cyber-physical systems such as Autonomous Driving Systems (ADS). Ensuring the correct behavior of such DNN-Enabled Systems (DES) is a crucial topic. Online testing is one ...

Assessing the quality of Deep Learning (DL) systems is crucial, as they are increasingly adopted in safety-critical domains. Researchers have proposed several input generation techniques for DL systems. While such techniques can expose failures, they do ...

The increase in number and types of various stakeholders interacting with self-driving vehicles expands the relevant automotive cybersecurity attack vectors that can be compromised. Furthermore, given the prominent role that human behavior plays in the ...

With the recent advances of Deep Neural Networks (DNNs) in real-world applications, such as Automated Driving Systems (ADS) for self-driving cars, ensuring the reliability and safety of such DNN-enabled Systems emerges as a fundamental topic in software ...

We present simulation scenarios of private and public transportation modes of transportation being replaced by autonomous mobility-on-demand (AMoD) system in Edinburgh, UK, based on realistic temporal and spatial demand patterns. Edinburgh is ...

Deep Learning (DL) has been successfully applied to a wide range of application domains, including safety-critical ones. Several DL testing approaches have been recently proposed in the literature but none of them aims to assess how different ...

The development of autonomous driving technology has been rapidly developing for the past decades. One key task in autonomous driving technology is the detection of objects around the vehicle. Radars are now more and more widely equipped by autonomous ...

Context-Aware Systems (a.k.a. CASs) integrate cyber and physical space to provide context-aware adaptive functionalities. Building context-aware systems is challenging due to the uncertainty of the real physical environment. Therefore, input validation ...

Ethical and social problems of the emerging technology of self-driving cars can best be addressed through an applied engineering ethical approach. However, currently social and ethical problems are typically being presented in terms of an idealized ...

Efficiency gains in economic processes often do not deliver the projected overall savings. Irrespective of whether the increase in efficiency saves energy, resources, time or transaction costs, there are various mechanisms that spur additional ...

In order to make the experience of traveling in automated vehicles more enjoyable, Virtual Reality (VR) experiences based on the real-world journey have been proposed. Presenting users with VR content synched to the car's actual movement decreases the ...

Autonomous driving carries the promise to drastically reduce the number of car accidents; however, recently reported fatal crashes involving self-driving cars show that such an important goal is not yet achieved. This calls for better testing of the ...

Self-driving cars rely on software which needs to be thoroughly tested. Testing self-driving car software in real traffic is not only expensive but also dangerous, and has already caused fatalities. Virtual tests, in which self-driving car software is ...

Autonomous driving carries the promise to drastically reduce the number of car accidents; however, recently reported fatal crashes involving self-driving cars show this important goal is not yet achieved, and call for better testing of the software ...

Autonomous driving carries the promise to drastically reduce car accidents, but recently reported fatal crashes involving self-driving cars suggest that the self-driving car software should be tested more thoroughly. For addressing this need, we ...

Ensuring the safety of self-driving cars is important, but neither industry nor authorities have settled on a standard way to test them. Deploying self-driving cars for testing in regular traffic is a common, but costly and risky method, which has ...