Mixed-Autonomy Traffic

Traffic congestion is a serious problem.
The developments of autonomous cars are going with a rapid pace. While autonomous cars are expected to provide a safe and comfortable driving experience, there can be more that they offer. In ILIAD, we are investigating the effects of autonomous cars on the traffic to see how they can increase road capacities and so beat traffic congestion.

Influencing Human Policies

Autonomus cars influence human drivers so that they can align on the roads for higher efficiency.
In addition to the platooning capabilities of autonomous vehicles, they also have the ability to influence other drivers' behaviors. We develop interaction-level policies for autonomous cars to increase the efficiency of the traffic networks by influencing human drivers and maximizing the gain obtained from platooning.

Influencing Routing Policies

Optimal routing schemes significantly improve traffic conditions.
From a higher-level perspective, it is possible to reduce traffic congestion by carefully optimizing for the autonomy level of the roads. To achieve that, we develeop control algorithms that optimize autonomous cars' routing choices to make sure all cars will experience the minimum possible latency. We generalize our works in both absence and presence of altruistic drivers who can (be made) take longer routes for social good.

Incomplete List of Related Publications:
  • Daniel A. Lazar, Kabir Chandrasekher, Ramtin Pedarsani, Dorsa Sadigh. Maximizing Road Capacity Using Cars that Influence People. Proceedings of the 57th IEEE Conference on Decision and Control (CDC), December 2018. [PDF]
  • Erdem Bıyık, Daniel A. Lazar, Ramtin Pedarsani, Dorsa Sadigh. Altruistic Autonomy: Beating Congestion on Shared Roads. Proceedings of the 13th International Workshop on Algorithmic Foundations of Robotics (WAFR), December 2018. [PDF]