Science and Technology

Pedestrian and cyclist interaction with autonomous vehicles

AV/pedestrian interaction

It is uncertain how autonomous vehicles (AV) will affect vulnerable road users, particularly as concerns the behaviour of pedestrians and cyclists. This study will investigate AV/pedestrian interaction and AV/cyclist interaction.

Approximately 60,000 pedestrians are killed worldwide each year, with 40% caused by driver behaviour. Autonomous vehicles are expected to be safer than human drivers as they are not susceptible to distractions, drink driving, traffic violations or fatigue. However, several challenges need to be addressed before AVs can fully integrate the transport system, especially conflicts with vulnerable road users.

Two surveys will be conducted. The first survey will help classify pedestrian and cyclist behaviour, and the second survey will measure pedestrians and cyclist receptivity towards AVs.

Additionally, we will conduct a virtual reality experiment to evaluate the interaction of vulnerable road users with AVs.

Participants

Project background

In the near future, AVs will operate in the transport system. This mode of transport is entering a period of development maturity and significant progress. It’s progress can be measured by the increasing number of kilometres driven by AVs, countries’ readiness to introduce AVs safely, and the exponential growth of research focused on AVs.

This progress has also uncovered complex challenges of implementation for AVs. One of the most significant challenges faced by AVs is pedestrian and cyclist interaction, and possible conflicts between them. In order to avoid conflict, AVs will have to detect and predict pedestrian and cyclist behaviour accurately. Researchers have made significant progress in terms of AVs’ hardware and software, but less improvement in understanding the behaviour of pedestrians and cyclists when interacting with AVs.

See the full list of iMOVE projects here

This study investigates AV/pedestrian interaction and AV/cyclist interaction to help introduce AVs to society safely and effectively. It will be conducted in three parts: two surveys, and one experiment. The first survey will allow us to classify pedestrian and cyclist behaviour in Australia using five categories:

  • Violations
  • Errors
  • Lapses
  • Positive behaviours
  • Aggressive behaviours

The second survey will measure pedestrians and cyclist receptivity towards AVs utilising the Theory of Planned Behaviour. We will also identify factors with the most significant impact on pedestrian and cyclist behaviour.

The third part is a Virtual Reality Experiment (VRE) to evaluate AV/pedestrian interaction. The VRE will help us to expose pedestrian to AVs and therefore measure a hypothesis change between the observed pedestrian behaviour and the self-reported pedestrian behaviour (obtained from the second survey described above).

The VRE will include AVs and human-driven vehicles (with and without communication features) and virtual pedestrians. The communication features attached to the virtual AV will assess a possible improvement in AV/pedestrian interaction.

The results will help engineers and researchers to create effective strategies to improve pedestrian behaviour, AV companies to improve vehicle-pedestrian and vehicle-cyclist interaction, and governments to shape traffic regulations for AVs, pedestrians and cyclists.

Project objectives

The analyses of AV–pedestrian and AV–cyclist interaction to help the integration of AVs to the transport system safely and effectively.

Pedestrian interaction and AVs

  • Categorise Australian pedestrian behaviours into violations, lapses, errors, positive behaviours, and aggressive behaviours, from the Pedestrian Behaviour Survey.
  • Identify which factors influence the behaviour of pedestrians crossing in front of AVs.
  • Predict the behaviour of pedestrians crossing in front of AVs.
  • Compare the simulated behaviour of pedestrians crossing in front of AVs, to the simulated behaviour of pedestrians crossing in front of human-driven vehicles.
  • Measure the effect of AV-pedestrian communication channels, on the behaviour of pedestrians crossing in front of AVs.
  • Compare the self-reported behaviour of pedestrians crossing in front of AVs before, and after interacting with AVs in a virtual environment.
  • Compare self-reported and simulated behaviour of pedestrians crossing in front of AVs

Cyclist interaction and AVs

  • Categorise Australian cyclist behaviours using the Cyclist Behaviour Survey
  • Identify which factors influence the behaviour of cyclist interaction

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