Towards connected autonomous driving: review of use-cases

Montanaro, Umberto; Dixit, Shilp; Fallah, Saber; Dianati, Mehrdad; Stevens, Alan; Oxtoby, David; Mouzakitis, Alexandros

doi:10.1080/00423114.2018.1492142

Cited by 150 publications

(75 citation statements)

References 125 publications

(214 reference statements)

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“…We are seeing an increased introduction of automation technology into passenger cars and heavy goods vehicles, with the goal to ultimately reach fully automated vehicles (AVs). AVs offer the potential for significant reductions in accident rates, emissions and traffic congestion [1]. To that end, much research and development has gone into systems that automatically maintain adequate speed and keep within the lane by applying steering movements to aid the driver [2].…”

Section: Introductionmentioning

confidence: 99%

“…A common approach to validate AVs in simulation is to conduct Monte Carlo experiments, where scenarios are randomly simulated. Both, public road trials and Monte Carlo simulation suffer from the same disadvantage however, which is that most 1 Institute for Future Transport and Cities, Coventry University, Coventry CV1 5FB, United Kingdom 2 HORIBA MIRA, Nuneaton CV10 0TU, United Kingdom…”

Section: Introductionmentioning

confidence: 99%

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Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Batsch

Daneshkhah

Cheah

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Safety is an essential aspect in the facilitation of automated vehicle deployment. Current testing practices are not enough, and going beyond them leads to infeasible testing requirements, such as needing to drive billions of kilometres on public roads. Automated vehicles are exposed to an indefinite number of scenarios. Handling of the most challenging scenarios should be tested, which leads to the question of how such corner cases can be determined. We propose an approach to identify the performance boundary, where these corner cases are located, using Gaussian Process Classification. We also demonstrate the classification on an exemplary traffic jam approach scenario, showing that it is feasible and would lead to more efficient testing practices.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Batsch

Daneshkhah

Cheah

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

View full text Add to dashboard Cite

show abstract

“…With the steady increase in the number of vehicles on the road, issues such as traffic congestion, pollution, and road safety are becoming critical issues [3]. Autonomous vehicles have gained significant interest as solutions to these challenges [4]- [7]. For instance, 90% of all car accidents are estimated to be caused by human errors, while only 2% are caused by vehicle failures [8].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Kuutti

Bowden

Jin

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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489

182

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Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

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“…Autonomous vehicles are proposed as the future of intelligent transportation systems to address problems such as traffic congestion, pollution, and road safety [4,14,16,19]. Deep learning has emerged as a popular artificial intelligence technique for autonomous vehicles, and has been proposed for many uses in autonomous vehicles, including vehicle control [13].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Data Engineering and Automated Learning – IDEAL 2019

2019

Lecture Notes in Computer Science

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Deep learning is a promising class of techniques for controlling an autonomous vehicle. However, functional safety validation is seen as a critical issue for these systems due to the lack of transparency in deep neural networks and the safety-critical nature of autonomous vehicles. The black box nature of deep neural networks limits the effectiveness of traditional verification and validation methods. In this paper, we propose two software safety cages, which aim to limit the control action of the neural network to a safe operational envelope. The safety cages impose limits on the control action during critical scenarios, which if breached, change the control action to a more conservative value. This has the benefit that the behaviour of the safety cages is interpretable, and therefore traditional functional safety validation techniques can be applied. The work here presents a deep neural network trained for longitudinal vehicle control, with safety cages designed to prevent forward collisions. Simulated testing in critical scenarios shows the effectiveness of the safety cages in preventing forward collisions whilst under normal highway driving unnecessary interruptions are eliminated, and the deep learning control policy is able to perform unhindered. Interventions by the safety cages are also used to retrain the network, resulting in a more robust control policy.

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Towards connected autonomous driving: review of use-cases

Cited by 150 publications

References 125 publications

Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

A Survey of Deep Learning Applications to Autonomous Vehicle Control

Intelligent Data Engineering and Automated Learning – IDEAL 2019

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