SmoothRide leverages advanced AI to analyze and improve infrastructure, making it safer and more enjoyable for all. By integrating data from various sources, including citizen feedback and AI-driven insights, the platform helps redesign urban environments to promote safety and livability. This leads to better biking lanes, more pedestrian-friendly areas, and overall improved traffic management. SmoothRide aims to engage communities and local authorities in building future-ready infrastructures that are both functional and pleasant to live in.

AutoDRIVE Cooperative MARL is an open-source framework designed to train and deploy cooperative multi-agent reinforcement learning (MARL) policies for autonomous driving tasks. It integrates with realistic simulators to model traffic scenarios like intersections, highway platooning, and merging. The framework implements centralized training with decentralized execution, enabling vehicles to learn shared policies that maximize overall traffic efficiency and safety. Users can configure environment parameters, choose from baseline MARL algorithms, visualize training progress, and benchmark agent coordination performance.