P3Mobility successfully integrated with Advanced Mobility Analytics Group’s (AMAG) state of the art computer vision enabled SMART Platform to create and broadcast SAE J3224 Sensor Data Sharing Messages (SDSMs) for cooperative perception. The SDSMs are being broadcast at the Mcity Test Facility – the University of Michigan’s connected and automated vehicle proving ground – with the aim of delivering improved vulnerable road user (VRU) safety capabilities.
“We are very excited to be partnering with AMAG to deliver this new capability,” said P3Mobility Director of Operations Jeremy Ward. “Vulnerable road user safety is a crisis in the United States where pedestrian traffic fatalities are at a 40-year high. Delivering these safety messages will pave the way to safer roads for all.”
P3Mobility’s software platform enables V2X Roadside Units (RSUs) to broadcast SDSMs in real time. This allows both autonomous vehicles and human drivers to receive information on VRUs that may be out of sight of the vehicle. Broadcasting these messages is important for the growth of the V2X ecosystem because the majority of VRUs – such as pedestrians and bicyclists – are not able and are not expected to have devices that directly communicate with V2X infrastructure and vehicles. The SAE J3224 message standard establishes a common message format for V2X participants to share and build consensus on detected VRU presence, enabling vehicles, drivers, and infrastructure to enhance VRU safety.
The detection of VRUs is enabled by Advanced Mobility Analytics Group’s SMART Platform. SMART is deployed in AWS’s Panorama (Edge computing device) to deliver low-latency computer vision machine learning models from existing video or LiDAR sensors to detect road users and track their trajectories measured in milliseconds. The technology continuously monitors traffic at intersections, mid-block crossings, and other sites to provide safety critical VRU data to P3Mobility’s platform for the creation and broadcast of the SDSM.
“The opportunity to partner with P3Mobility to improve the safety of VRUs in connected corridors for our customers is extremely exciting”, said Simon Washington, AMAG’s CEO. “The number of potential applications we are discussing with partners like P3 Mobility to improve both safety and operations through V2X applications is growing rapidly, with high-impact applications already identified in freight priority, rail, and VRU safety in both urban and rural settings.”
In addition to J3224 SDSM creation and broadcast, P3Mobility is also developing methods to enhance VRU safety systems, automated driving systems, and next-generation ADAS technologies with expanded ground truth-trained and validated high-confidence perception solutions. Ground truth trained and validated perception systems are a prerequisite for any widely accepted and deployed VRU Safety System implementation. Additionally, it will become increasingly imperative to validate Automated Driving Systems and the Next Generation ADAS technologies with high confidence perception data.
For more information on how P3Mobility and AMAG solutions can support your road safety mandates, automated driving, or intelligent transportation use cases, please contact the following:
330 E Liberty St
Ann Arbor, MI 48104
Advanced Mobility Analytics Group
l5/80 Ann St, Brisbane City QLD 4000
Who We Are
P3Mobility is a digital infrastructure project development firm which provides consulting services and a software platform that enables a sustainable commercial business model in the V2X ecosystem. P3Mobility’s connected vehicle platform provides a road operator or public jurisdiction with the means of financing, operating, and maintaining V2X and other smart infrastructure through the facilitation of various connected vehicle services.
Advanced Mobility Analytics Group (AMAG) builds technology solutions to help transportation agencies reduce fatalities and crashes by proactively identifying and responding to road user safety risks. The company’s expertise is to use research-tested computer-vision Machine Learning models and video analytic at the edge and in the cloud to detect, classify, track, analyze, and alert traffic engineering, operations, planning, and road-users of critical safety and operating risks.