Researchers in Australia say they have developed a technology that allows autonomous vehicles to track moving pedestrians hidden behind buildings and cyclists obscured by cars, trucks and buses.
The technology has been refined as part of a project funded by the iMove Cooperative Research Centre in collaboration with the University of Sydney’s Australian Centre for Field Robotics and Australian connected vehicle company Cohda Wireless. iMove recently released its new findings in a final report following three years of research and development.
The approach relies on collective perception (CP) (also known as cooperative perception). Using roadside ITS (Intelligent Transport System) information sharing units (‘ITS stations’), vehicles can share what they ‘see’ with others using vehicle-to-X (V2X) communication. The system significantly increases the vehicle’s range of perception by allowing it to tap into various viewpoints.
“This is a game changer for both human-operated and autonomous vehicles which we hope will substantially improve the efficiency and safety of road transportation,” said Professor Eduardo Nebot from the Australian Centre for Field Robotics.
“Using collective perception, the connected vehicle was able to track a pedestrian visually obstructed by a building. This was achieved seconds before its local perception sensors or the driver could possibly have seen the same pedestrian around the corner, providing extra time for the driver or the navigation stack to react to this safety hazard,” he added.
Another experiment demonstrated how collective perception could allow vehicles to safely interact with walking pedestrians, with the vehicle’s response based on the perception information provided by a roadside ITS station.
The three-year project also demonstrated the expected behavior of a connected vehicle when interacting with a pedestrian rushing towards a designated crossing area.
“Using the ITS system, the connected autonomous vehicle managed to take pre-emptive action: braking and stopping before the pedestrian crossing area based on the predicted movement of the pedestrian,” Nebot noted.
“The pedestrian tracking, prediction, path planning and decision making were based on the perception information received from the ITS roadside stations. Collective perception enables the smart vehicles to break the physical and practical limitations of onboard perception sensors.”
The project’s lead researcher, Dr Mao Shan, concluded that the research confirmed using CP could improve awareness of vulnerable road users and safety in many traffic scenarios.
“Our research has demonstrated that a connected vehicle can ‘see’ a pedestrian around corners,” Shan said.
“More importantly, we demonstrate how connected autonomous vehicles can safely interact with walking and running pedestrians, relying only on information from the ITS roadside station.”
