Virtual assistants and self-driving cars

Lugano, Giuseppe

doi:10.1109/itst.2017.7972192

Cited by 54 publications

(20 citation statements)

References 11 publications

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“…Considering some specificities of autonomous truck and its risks, at least a few more studies about the topic could be expected. [64], [67], [70], [61], [65], [69], [62], [58], [63], [71], [72], [74], [68], [ [39], [18], [32], [31], [33], [26], [28], [30], [55], [52], [29], [ [75], [41], [29], [20], [44], [35], Prediction of adc vanced driver assistance systems (ADAS) remaining useful life (RUL) for the prognosis of ADAS safety critical components Pedestrian Detection; How to "automate" manual annotation for images to train visual perception for AVs Road junction detection; [52], [27], [37], [30] Bayesian Artificial Intelligence…”

Section: Final Remarksmentioning

confidence: 99%

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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Autonomous Vehicles (AV) are expected to bring considerable benefits to society, such as traffic optimization and accidents reduction. They rely heavily on advances in many Artificial Intelligence (AI) approaches and techniques. However, while some researchers in this field believe AI is the core element to enhance safety, others believe AI imposes new challenges to assure the safety of these new AI-based systems and applications. In this non-convergent context, this paper presents a systematic literature review to paint a clear picture of the state of the art of the literature in AI on AV safety. Based on an initial sample of 4870 retrieved papers, 59 studies were selected as the result of the selection criteria detailed in the paper. The shortlisted studies were then mapped into six categories to answer the proposed research questions. An AV system model was proposed and applied to orient the discussions about the SLR findings. As a main result, we have reinforced our preliminary observation about the necessity of considering a serious safety agenda for the future studies on AIbased AV systems.

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Section: Final Remarksmentioning

confidence: 99%

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

Nascimento

Vismari

Molina

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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“…In the case of human-driven connected vehicles, the interface between the system and the driver has to be designed in a way that notifications and the information presented to the driver should not distract her/his attention, while the risk that drivers are not notified should be minimized. In [35], Lugano considers a usage of virtual assistants to accomplish similar tasks. For simplicity reasons, we assume in the simulations that the driver will notice the message immediately after its reception.…”

Section: Unobtrusivenessmentioning

confidence: 99%

A Feasibility Study of Privacy Ensuring Emergency Vehicle Approaching Warning System

Petrov

Počta

Roman³

et al. 2019

Applied Sciences

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With the deployment of Vehicular Ad hoc Networks (VANETs), new transport safety and efficiency applications are emerging. One of the fields where the adoption of information and communication technologies (ICT) is expected to bring great benefits, is emergency systems. A properly designed emergency vehicle warning system should provide car drivers with adequate reaction times and additional information, complementing the currently used lights and sirens. The objective is to increase road safety and to create conditions for a fast and reliable movement of emergency vehicles (EVs). The available literature addressing privacy issues in VANET-based emergency vehicle warning systems is strongly limited. In this paper, the privacy ensuring emergency vehicle approaching warning system (PEEV-WS) is proposed based on a requirements analysis. Privacy is ensured by avoiding transmissions of sensitive information (e.g., expected EV route) over the wireless channel. This is achieved by assigning the decision-making responsibility to an EV and determining which vehicles can potentially interfere with the EV in the near future, and to notify those vehicles only by unicasting vehicle-customized information. The performance of the system is evaluated by federated telco-traffic simulations in terms of end-to-end delay and message delivery probability for three commonly used ad hoc routing protocols—Ad hoc On-Demand Distance Vector (AODV), Greedy Perimeter Stateless Routing (GPSR), and Dynamic MANET On-demand (DYMO), as well as in terms of reaction time, which the system provides to the drivers. Despite low applicability of the ad hoc routing protocols for vehicular communication, especially for low-latency and high-frequency applications, the simulations of the communication network demonstrate that the AODV protocol, with the modified configuration, can support the emergency vehicle warning system. The traffic simulations confirm that the system has the potential to provide drivers with sufficient reaction time.

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“…2 The design of HRI strategies is still a difficult problem, in part for the human factors complexity. 3 Several HRI approaches have been studied for different problems such as: vehicle navigation, 4,5 humanoids, 6 rescue robots, 7 assistant robots, 8 collaborative robots, 9 and among others. However, HRI is a relatively new research field which is still under development; therefore, many of its guidelines and design criteria may depend on the context and application taxonomy.…”

Section: Introductionmentioning

confidence: 99%

“…11 There are several approaches that have used HRI methods for vehicle-driving applications, for example, using a robotic architecture as an accident-prevention safety system intended to give information and prevent if a risky situation arises. 12 The research carried out by Lugano 5 is focused on virtual assistants and studies their role and functions for automated vehicle applications. Other works have studied collision avoidance among robots and humans using extrapolation of human intentions and optimization methods.…”

Section: Introductionmentioning

confidence: 99%

On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters

Vásconez

Carvajal

Cheein

2019

Advances in Mechanical Engineering

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A proper design of human–robot interaction strategies based on human cognitive factors can help to compensate human limitations for safety purposes. This work is focused on the development of a human–robot interaction system for commercial vehicle (Renault Twizy) driving, that uses driver cognitive parameters to improve driver’s safety during day and night tasks. To achieve this, eye blink behavior measurements are detected using a convolutional neural network, which is capable of operating under variable illumination conditions using an infrared camera. Percentage of eye closure measure values along with blink frequency are used to infer diver’s sleepiness level. The use of such algorithm is validated with experimental tests for subjects under different sleep-quality conditions. Additional cognitive parameters are also analyzed for the human–robot interaction system such as driver sleep quality, distraction level, stress level, and the effects related to not wearing glasses. Based on such driver cognitive state parameters, a human–robot interaction strategy is proposed to limit the speed of a Renault Twizy vehicle by intervening its acceleration and braking system. The proposed human–robot interaction strategy can increase safety during driving tasks for both users and pedestrians.

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Virtual assistants and self-driving cars

Cited by 54 publications

References 11 publications

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

A Systematic Literature Review About the Impact of Artificial Intelligence on Autonomous Vehicle Safety

A Feasibility Study of Privacy Ensuring Emergency Vehicle Approaching Warning System

On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters

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