A good deal of autonomous vehicle research—and certainly the bulk of AV and ADAS (advanced driver-assistance systems) reporting—focuses on how AVs interact with traffic on the individual and group level or how vehicle owners interact with their ADAS-equipped vehicles.
But there is more to the world than the ownership. Improving road safety goes far beyond how occupants interact with their own vehicle or vehicles interact with each other. For example, in 2021, WHO estimated that roughly 68,000 Chinese pedestrians are killed each year in what is now being called “distracted walking” accidents. That’s more than a quarter of all Chinese road fatalities and has driven both commercial property holders and municipalities to begin experimenting with engineering greater pedestrian safety into paths and roadways.
For context: The US and China have nearly the same number of cars on the road, but the US has fewer than 1/10th as many pedestrian fatalities each year. But even in the US pedestrian fatalities rates are climbing. The American Academy of Orthopaedic Surgeons (AAOS) has begun working to raise awareness of the danger non-vehicle roadway users pose to themselves and others.
This is a potent reminder that any given roadway is a complex environment where motor vehicles, cyclists, people, animals, civil engineering, architecture, and weather all interact to create a unique set of constantly changing conditions.
RTI Simulations are Key to Autonomous Vehicle Research
An increasing amount of research is looking into these complex interactions, relying on networked systems of immersive simulators. (This is similar to the network “pods” of vehicle simulators the US military uses to train motorized cavalry or bomb sweeping units.)
For example, for years Oregon State University (OSU) has used networked sets of RTI motor vehicle and bicycle simulators to explore the complex interactions between the built environment, vehicles, and all the users of a transportation system. RTI sims are especially well-suited to such studies. In part, that’s due to the excellent hardware/software integration, with immersive full-size cabs and bicycles made from actual manufacturer components. RTI is also praised for the fidelity of their ambient traffic model. These excel at both micro (e.g., individual) and meso (group) traffic behavior.
Research institutions and automakers worldwide rely on RTI platforms for their roadway safety research. According to OSU, “The high fidelity simulators allow researchers to evaluate many more scenarios that would be practically possible in other experimental mediums while simultaneously controlling for extraneous variables.”