Autonomous intelligent cars: proof that the EPSRC Principles are future-proof

Buning, Madeleine de Cock; Bruin, R. de

doi:10.1080/09540091.2017.1310181

Connection Science

2017

DOI: 10.1080/09540091.2017.1310181

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Autonomous intelligent cars: proof that the EPSRC Principles are future-proof

Madeleine de Cock Buning

R. de Bruin

Abstract: Principle 2 of the EPSRC's principles of robotics (AISB workshop on Principles of Robotics, 2016) proves to be future proof when applied to the current state of the art of law and technology surrounding autonomous intelligent cars (AICs). Humans, not AICS, are responsible agents. AICs should be designed; operated as far as is practicable to comply with existing laws and fundamental rights and freedoms, including privacy by design. It will show that some legal questions arising from autonomous intelligent drivi… Show more

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Cited by 7 publications

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“…Autonomous vehicle system is very complex, has a lot of sensors data process and machine learning tasks, which always inadvertently beyond on-board computer process power, Liu S. et al proposed a unified cloud platform to enhanced performance and energy efficiency of autonomous driving application in cloud platform. de Cock Buning, M. et al discussed the moral and legal questions about autonomous driving(de Cock Buning, M., et al, 2017).…”

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An edge streaming data processing framework for autonomous driving

et al. 2020

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In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. In view of the above problems, the proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realize the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimize the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high.

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An edge streaming data processing framework for autonomous driving

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A shared libraries aware and bank partitioning-based mechanism for multicore architecture

Yang

Chen

et al. 2023

Soft Comput

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Autonomous road vehicles: recent issues and expectations

Skrickij

Šabanovič

Žuraulis

2020

IET Intelligent Transport Systems

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Autonomous intelligent cars: proof that the EPSRC Principles are future-proof

Cited by 7 publications

References 7 publications

An edge streaming data processing framework for autonomous driving

An edge streaming data processing framework for autonomous driving

A shared libraries aware and bank partitioning-based mechanism for multicore architecture

Autonomous road vehicles: recent issues and expectations

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