Autonomous Driving R&D — Perception, Planning & Control

Full-stack autonomy from sensor fusion and deep learning to motion planning and vehicle control.

MotiveLab's autonomous driving team builds perception, planning, and control stacks for self-driving vehicles, shuttles, and autonomous robots. We integrate LiDAR, camera, and radar sensors with deep learning pipelines to deliver reliable autonomy in real-world environments. Our expertise spans the full autonomy software stack along with the hardware integration needed to make it work.

What We Offer

  • Perception — object detection, tracking, semantic segmentation, and scene understanding
  • Localization — GPS+IMU fusion, visual odometry, LiDAR matching, and map-based localization
  • Path planning — global planning (A*, RRT*) and local planning (DWA, TEB, MPC)
  • Vehicle control — longitudinal and lateral control with PID, MPC, and model-based methods
  • System integration — sensor mounting, timing synchronization, compute platform selection

🛸 We have deployed autonomous navigation systems on shuttles, agricultural vehicles, and industrial AGVs.

Technologies

  • LiDAR / Camera / Radar fusion — sensor calibration, alignment, and multi-modal detection
  • Deep learning — TensorRT, YOLO, PointPillars, and custom CNN deployment
  • SLAM — visual SLAM, LiDAR SLAM, and multi-sensor map building
  • MPC / PID control — path tracking, velocity regulation, and smooth trajectory execution
  • ROS2 / Autoware — production-grade autonomous driving middleware
  • V2X communication — DSRC, C-V2X, and teleoperation interfaces

Applications

  • Autonomous robots — service robots, delivery robots, companion robots
  • Self-driving shuttles — low-speed people movers for campuses and parks
  • Agricultural autonomy — autonomous tractors, harvesters, and sprayers
  • Industrial AGV/AMR — factory floor transport and warehouse logistics
Discuss Your Autonomy Project