Get the latest tech news
Scaling Up Reinforcement Learning for Traffic Smoothing
The BAIR Blog
In our case, the environment is a mixed-autonomy traffic scenario, where AVs learn driving strategies to dampen stop-and-go waves and reduce fuel consumption for both themselves and nearby human-driven vehicles. To achieve this, we leveraged experimental data collected on Interstate 24 (I-24) near Nashville, Tennessee, and used it to build simulations where vehicles replay highway trajectories, creating unstable traffic that AVs driving behind them learn to smooth out. The typical behavior learned by the AVs is to maintain slightly larger gaps than human drivers, allowing them to absorb upcoming, possibly abrupt, traffic slowdowns more effectively.
Or read this on Hacker News