Human Models
Interactions
Societal Implications

Mixed-Autonomy Traffic

The development 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 take our multi-agent interaction work to the space of mixed-autonomy traffic networks for the goal of analyzing societal implications of autonomy. 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. Our work is one of the first to connect micro-level vehicle interactions with macro-level traffic models [CDC 2018].

Influencing Routing Policies

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 first analyze different equilibria behavior (Nash Equilibria, Best Nash Equilibria, Robust Best Nash Equilibria) that emerges in mixed-autonomy traffic. We introduce the notion of altruistic autonomy that models how a fleet of autonomous vehicles can act altruistically (as opposed to selfishly), by accepting a larger delay. We analyze Best Altruistic Nash Equilibria that can reduce congestion on parallel traffic networks [WAFR 2018]. We further develop reinforcement learning 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, as well as in the presence of perturbations such as accidents [arXiv].

Incomplete List of Related Publications:
  • Daniel A. Lazar*, Erdem Bıyık*, Dorsa Sadigh, Ramtin Pedarsani. Learning How to Dynamically Route Autonomous Vehicles on Shared Roads. Submitted to IEEE Transactions on Control of Network Systems (TCNS).[PDF]
  • Erdem Bıyık, Daniel A. Lazar, Ramtin Pedarsani, Dorsa Sadigh. The Green Choice: Learning and Influencing Human Decisions on Shared Roads. Proceedings of the 58th IEEE Conference on Decision and Control (CDC), December 2019. [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]
  • 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]