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Tractable situation coverage for autonomous cars

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Research areas: Autonomous and self-adaptive systems; Safety analysis, system safety; Safety of autonomous and self-adaptive systems; Software testing

It is hard to test autonomous robot (AR) software because of the range and diversity of external situations (terrain, obstacles, humans, peer robots) that the robots must deal with. AR must interpret a wide variety of stimuli from their world and make decisions that are appropriate given the specific combination of stimuli they are receiving – their relationship with their environment is complex. Many features of the environment that matter are not specific to the mission goals – they are simply present, and must be handled. Beyond merely needing to avoid accidents, AR may need to comply with detailed-yet-ambiguous rules intended to guide human behaviour (such as the UK Highway Code and Rules of the Air).

Situation coverage [1] as a potentially important technique for testing AR. Put simply, situation coverage is a measure of the proportion of all possible situations (that the software under test could conceivably encounter) that have been tested by some test set. Like any coverage criterion, situation coverage can be used to assess the adequacy of a test set, and to guide automated test generation.

There are some existing models of situation spaces for 3D scenes [2]. To date, however, no-one has published a tractable and interesting situation space model for an autonomous vehicle.

In this PhD project, you could:

• Design and justify a situation space model for an autonomous car
• Implement situation generation (in simulation) based on that
• Evaluate how effective the resulting testing is at finding seeded faults in a simulated autonomous vehicle

References

References
[1] Situation coverage - a coverage criterion for testing autonomous robots. Rob Alexander, Heather Hawkins, Drew Rae. Technical Report YCS-2015-496, Department of Computer Science, University of York, Jan 2015 https://www.cs.york.ac.uk/ftpdir/reports/2015/YCS/496/YCS-2015-496.pdf

[2] O. Zendel, W. Herzner, and M. Murschitz, ‘VITRO - Model based vision testing for robustness’, in 2013 44th International Symposium on Robotics (ISR), 2013, pp. 1–6. https://doi.org/10.1109/ISR.2013.6695667

Related Subjects

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