Urban population grows at a much higher rate than transportation infrastructure, and yet people continue to mostly use cars for their trips. Multimodal transportation provides an approach to use a combination of existing infrastructure and modes of transportation that help complete all the different segments of a trip. The big drawback for multimodal transportation, on the other hand, is the time lost when changing transportation modes.

As with any trip, there are different criteria which can be taken into consideration when evaluating a selected route. The goal of this project is to evaluate how multimodal transport compares against single mode in respect to travel time and energy consumption, especially in the context of autonomous driving.

Student teams will develop autonomous interfaces for both small cars and drones, and duckies will act as single passengers in both transportation modes. Student teams get points for detecting QR codes in a parkour, where the codes are distributed between ground and walls, so that teams can only get many points if they change modes.

Tags: Mobile robotics, Autonomous driving, Robot Operating System,
Computer vision, Machine learning, Robot cooperation