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Safe Deep Neural Network-Driven Autonomous Vehicles Using Software Safety Cages

  • Sampo KuuttiEmail author
  • Richard Bowden
  • Harita Joshi
  • Robert de Temple
  • Saber Fallah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11872)

Abstract

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 re-train the network, resulting in a more robust control policy.

Keywords

Automatic control Autonomous vehicles Cyber-physical systems Deep learning Safety 

Notes

Acknowledgments

This work was supported by the UK-EPSRC grant EP/R512217/1 and Jaguar Land Rover.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Connected Autonomous Vehicles LabUniversity of SurreyGuildfordUK
  2. 2.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK
  3. 3.Jaguar Land RoverCoventryUK

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