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Artificial intelligence to help autonomous vehicles avoid idling at red lights
No one likes waiting at a red light. But how far are we from the time when Autonomous Vehicles will be able to avoid all the intersections coming in their route? Maybe not too far!
Also, signalized intersections aren’t just a minor nuisance for drivers; vehicles consume fuel and emit greenhouse gases while waiting for the light to change.
While humans may drive past a green light without giving it much thought, intersections can present billions of different scenarios depending on the number of lanes, how the signals operate, the number of vehicles and their speeds, the presence of pedestrians and cyclists, etc.
Researchers working on Connected and Autonomous Vehicles have provided various possible solutions to this problem. One of the approaches been studied recently by scientists is to implement collision avoidance algorithms to make a vehicle pass through an intersection without touching any other vehicle. This approach works by calculating the trajectories of all nearby vehicles and then every vehicle increases or decreases its speed to make sure no two vehicles are at the same position at the same time.
These approaches are quite interesting for the readers, and they do give good results on testing environments as well. But there is one major hurdle to pass. Most of these approaches require all the vehicles on the roads to be Autonomous and to share information among each other in order to give 100% results.
In my opinion, I don’t see that happening in the coming few years. As these approaches for tackling intersection control problems use mathematical models to solve one simple, ideal intersection. That looks good on paper, but likely won’t hold up in the real world, where traffic patterns are often about as messy as they come.
So, what are the other ways of tackling this issue? Let us discuss one more possible solution. What if motorists could time their trips so they arrive at the intersection when the light is green? While that might be just a lucky break for a human driver, it could be achieved more consistently by an autonomous vehicle that uses artificial intelligence to control its speed.
A recent study by MIT researchers captured my interest, they demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly.
Using simulations, they found that their approach reduces fuel consumption and emissions while improving average vehicle speed. The technique gets the best results if all cars on the road are autonomous, but even if only 25% use their control algorithm, it still leads to substantial fuel and emissions benefits.
Once they developed an effective control algorithm, they evaluated it using a traffic simulation platform with a single intersection. The control algorithm is applied to a fleet of connected autonomous vehicles, which can communicate with upcoming traffic lights to receive signal phase and timing information and observe their immediate surroundings. The control algorithm tells each vehicle how to accelerate and decelerate.
Their system didn’t create any stop-and-go traffic as vehicles approached the intersection. (Stop-and-go traffic occurs when cars are forced to come to a complete stop due to stopped traffic ahead). In simulations, more cars made it through in a single green phase, which outperformed a model that simulates human drivers. When compared to other optimization methods also designed to avoid stop-and-go traffic, their technique resulted in larger fuel consumption and emissions reductions. If every vehicle on the road is autonomous, their control system can reduce fuel consumption by 18% and carbon dioxide emissions by 25%, while boosting travel speeds by 20%.
Down the road, the researchers want to study interaction effects between multiple intersections. They also plan to explore how different intersection set-ups (number of lanes, signals, timings, etc.) can influence travel time, emissions, and fuel consumption. In addition, they intend to study how their control system could impact safety when autonomous vehicles and human drivers share the road. For instance, even though autonomous vehicles may drive differently than human drivers, slower roadways and roadways with more consistent speeds could improve safety.
While this work is still in its early stages, I see this approach as one that could be more feasibly implemented in the near-term.
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