Position: Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing

Autonomous vehicles (AVs), possibly using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization, may destabilize traffic networks, leading to longer travel times for human drivers. We study this interaction by simulating human drivers and AVs. Our experiments with standard MARL algorithms reveal that, both in simplified and complex networks, policies often fail to converge to an optimal solution or require long training periods. This problem is amplified by the fact that we cannot rely entirely on simulated training, as there are no accurate models of human routing behavior. In addition, real-world training in cities risks destabilizing urban traffic systems, increasing externalities, such as $CO_2$ emissions, and introducing non-stationarity as human drivers will adapt unpredictably to AV behaviors. In this position paper, we argue that city authorities must collaborate with the ML community to monitor and critically evaluate the routing algorithms proposed by car companies toward fair and system-efficient routing algorithms and regulatory standards.

Storyline

Experiments

./
├── experiments/
│   └── trials_on_two_routes/
│       └── exp_scripts/
│           └── ...

This directory contains all experiment scripts used for the Two-Route (Yield) (TRY) network. To run the experiments presented in this paper, navigate to the exp_scripts folder and execute:

python mutation{algorithm_name}.py

where {algorithm_name} specifies the MARL algorithm to be evaluated.