Undergraduate Research Winter/Fall 2026 Entrepreneurship Grade: #

kane@umich:~/projects/nift
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info --detailed
Info           Detail
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TIME            Jan 2026 – Present
LAB/ORG         Perot Jain TechLab, Mcity @ U-M; NiFT
ROLE            System Integration Engineer
STACK           Python, C++, ROS 2, CAN bus, Gazebo, Git, Linux
ros2 launch shuttle_simulation launch_sim.launch.py_

Mission

This project aims to develop an autonomous depot shuttle platform for Mcity in collaboration with NiFT. Our goal is to deliver a proof-of-concept transforming the NiFT Shuttle QB into an L4 autonomous vehicle by leveraging infrastructure-based sensing and routing.

As part of a six-person student team, I lead the system integration of by-wire controls, sensor suites, and data communication protocols. My specific contributions involve architecting the ROS 2 framework, mapping CAN bus communications, and developing high-fidelity simulation environments to validate navigation algorithms before physical deployment.

Implementation

I architected the ROS 2 workspace to achieve the Sense-Think-Act pipeline for the shuttle. For testing, simulation is also developed. The system can be split into 4 core layers:

Communication & Actuation

  • CAN Bus Integration: Utilized ros2_socketcan to interface with the vehicle’s control bus. I performed frame decoding using proprietary DBC files to map raw bus signals, including throttle, brake, gear, and steering.
  • By-Wire Controller: Developed a controller node to bridge high-level ROS 2 Twist commands with low-level CAN frames. This included implementing a Finite State Machine (FSM) to translate target velocities and steering angles into precise actuator setpoints.

Perception & Localization

  • LiDAR-Based Obstacle Detection: Processed raw point cloud data to extract obstacle coordinates and implemented spatial transformations (TF2) to map object detections from the sensor frame to the vehicle’s local coordinate system.
  • GNSS-Based State Estimation: Developed an integration for Mcity Octane via Socket.IO to stream global positioning data. Implemented a dual-beacon configuration to accurately compute the shuttle’s absolute heading and real-time coordinates.

Navigation & Planning

  • Decision Logic & Safety Layer: Implemented a sensor fusion strategy to synthesize LiDAR and GNSS data, enabling real-time decision-making for path progression and autonomous Emergency Stop (E-Stop) protocols.
  • Path Tracking & Velocity Control: Developed a Pure Pursuit controller integrated with Cross-Track Error (CTE) compensation to ensure high-fidelity waypoint following and stable velocity management.

Simulation

  • Environment Mirroring: Engineered a digital twin of the NiFt shuttle and Mcity depot within Gazebo and Rviz2, ensuring accurate physical modeling and sensor data replication.
  • Parity Testing: Structured the simulation nodes to be “plug-and-play” with the physical hardware nodes. This meant the exact same Planner and CAN nodes were used in both sim and reality, significantly reducing deployment bugs.

Challenges

CAN Bus & Hardware

Coming into the project with no prior CAN experience, the learning curve for low-level vehicle communication was steep.

  • CAN Frame: I moved beyond high-level code to understand the bit-level details of the DBC file. I had to learn how to manually manipulate bytes to input meaningful velocity and steering data into CAN frames.
  • Linux Networking: A major hurdle was configuring the Linux Kernel to recognize our Kvaser interface. I spent significant time troubleshooting the bridge between can-utils and the hardware, eventually successfully masking our laptop as a native node on the vehicle’s bus.

Software & API

The L4 aspect of NiFT shuttle relies on external data, which introduced unique software integration challenges.

  • Real-time External Comms: I had to implement a Socket.io client within our ROS 2 stack to communicate with the Mcity Octane API. Ensuring that infrastructure-based routing data reached our planner with minimal latency was critical for safe operation.
  • ROS 2 Ecosystem: Beyond just writing nodes, I had to master managing complex Parameter files, launch configurations, and package dependencies to ensure the system was modular and reproducible for the rest of the team.

Reflection

My biggest takeaway from this project is that building the NiFT autonomy stack from scratch gave me a transformative, under-the-hood look at how ROS 2 operates on physical hardware. Moving beyond simple simulations, I gained hands-on experience managing the problems of real-world systems, from synchronizing infrastructure-based sensor data via Socket.io to the nuances of bit-level CAN bus manipulation.

This whole experience pushed me toward the intersection of high-level intelligence and low-level execution. Now that I’ve built the vehicle comms and simulation from the ground up, I’m ready to dive into precise motion planning and non-smooth trajectory optimization. I want to take what I learned at PJTL and tackle more complex algorithms that let robots move through high-stakes environments with more mathematical rigor and agility.

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