RoboSense: Leveraging Robotaxi Fleets as Drive-by Sensors for Urban Traffic Monitoring

Authors: Yilin Wang, Yiheng Feng (Purdue University) Role: Individual Research Project Period: Apr 2023 – Present Status: Preprint, under review Code: GitHub — Dylan-Wyl10/RoboSense · Paper: Preprint PDF

Overview

Robotaxis are dispatched to carry passengers, but a centrally controlled fleet can also double as a network of drive-by sensors. RoboSense is a dynamic routing framework that makes traffic monitoring an explicit objective of fleet operation. It combines:

• a cell-based network representation aligned with vehicle sensing range, built on Cell Transmission Model (CTM) traffic-state prediction;
• a cell-level spatiotemporal coverage metric quantifying how well the fleet observes the network;
• a rolling-horizon MILP that trades off passenger travel time against network monitoring coverage.

The framework is evaluated in SUMO on a 5×5 urban grid network at 2%, 5%, and 10% robotaxi market penetration rates. A key finding: with appropriate objective weights, monitoring performance and robotaxi average speed improve simultaneously — better sensing also makes the fleet faster, a win-win that could incentivize operators to contribute vehicles as drive-by sensors.

Earlier Work: Dynamic Routing of CAVs for Improving Network Coverage

• Formulated the CAV routing problem considering network coverage as one objective.
• Proposed heuristic algorithms with greedy search to solve the multi-objective optimization.
• Conducted simulation experiments in a grid network to verify feasibility and relations between travel cost and network coverage.
• Outcome: Accepted by TRB Annual Meeting 2024 as poster presentation.