Connected and Autonomous Vehicles Research Group (CAVs)
Dr. Benjamas Panomruttanarug is the head of the Connected and Autonomous Vehicles (CAVs) research group. Since 2023, she has been appointed by KMUTT to lead the group in conducting research and securing funding in the field of autonomous vehicles. Her responsibilities include expanding both internal and external networks, with a particular emphasis on fostering international collaborations in autonomous driving research. The group’s autonomous vehicle prototypes include F1TENTH racing car robots, a go-kart, a golf cart, and, in the future, a BYD Dolphin. In addition, the group develops robotic arms for EV charging and is working towards deploying a wireless EV charging autonomous mobile robot (AMR).
Autonomous vehicle
LiDAR-Based 3D Mapping and Steer-by-Wire Platform
Our latest project features the BYD Dolphin electric vehicle, equipped with a LiDAR sensor for advanced mapping applications. By recording detailed point cloud data, we aim to generate high-resolution 3D maps of real-world environments. In the next phase, the platform will be modified with a steer-by-wire system—beginning with CAN signal investigation, in collaboration with our partners at TU Graz.
Autonomous golf cart
Integrated LiDAR, GNSS, IMU, and Vision Golf Cart
Watch our autonomous golf cart in action, integrating LiDAR, IMU, GNSS, and vision technologies. LiDAR, IMU, and GNSS are combined to generate a 2D map and navigate to the desired location, while the vision system enables the vehicle to stop when obstacles are detected ahead.
Autonomous golf cart
360° Camera Perception
Experience our autonomous golf cart equipped with four roof-mounted cameras—one on each side—to generate a real-time bird’s-eye view of its surroundings. This system is being developed for advanced perception tasks such as semantic segmentation, enabling the classification of objects, roads, and lanes for improved tracking and obstacle avoidance.
Autonomous golf cart
Vision-based autonomous navigation
Experience our autonomous golf cart as it navigates using vision-based technology.
Autonomous golf cart
GNSS-based autonomous navigation
We developed an autonomous navigation system for a golf cart using GNSS modules.
Autonomous golf cart
Autonomous speed control system
We developed an autonomous speed control system for the golf cart using a PID controller and compared our experimental results with those obtained from commercialized devices.
Autonomous go-kart
Road tracking using semantic segmentation
We developed a road tracking system for a go-kart robot using a RealSense camera and semantic segmentation to extract the road from other object classes. The system utilizes Iterative Learning Control (ILC) to ensure robustness in shade areas.
KUKA Robotic EV Charging System
Discover our KUKA robotic EV charging system, which leverages a RealSense camera to guide the robotic manipulator. By utilizing RGB data and point clouds, the system detects the vehicle charging inlet, while depth information is used to plan the robot’s motion path. Reinforcement learning techniques enable precise and adaptive positioning for efficient and autonomous EV charging.
UR5 Robotic EV Charger
We detected the EV inlet using an AI technique, and with the 3D point cloud data, the robot was able to successfully plug in without any collisions.
INC Racing 2020
Our first autonomous robot racing competition. We organized the first competition for our students this year.
