This paper presents the cooperative adaptive cruise control implementation of Team Mekar at the Grand Cooperative Driving Challenge (GCDC). The Team Mekar vehicle used a dSpace microautobox for access to the vehicle controller area network bus and for control of the autonomous throttle intervention and the electric-motor-operated brake pedal. The vehicle was equipped with real-time kinematic Global Positioning System (RTK GPS) and an IEEE 802.11p modem installed in an onboard computer for vehicle-to-vehicle (V2V) communication. The Team Mekar vehicle did not have an original-equipment-manufacturer-supplied adaptive cruise control (ACC). ACC/Cooperative adaptive cruise control (CACC) based on V2V-communicated GPS position/velocity and preceding vehicle acceleration feedforward were implemented in the Team Mekar vehicle. This paper presents experimental and simulation results of the Team Mekar CACC implementation, along with a discussion of the problems encountered during the GCDC cooperative mobility runs.

This paper proposes a vehicle detection and tracking system based on processing monochrome images captured by a single camera. The work has mainly been focused on detecting and tracking vehicles in daylight conditions, viewed from inside a vehicle. Unlike previous work, this approach uses vehicle shadow clues and vehicle edge information to obtain cost effective and fast estimation. The proposed method includes road area finding which has been implemented by a lane detection algorithm to avoid false detections of vehicles caused by the distraction of background objects. Assuming that lanes are successfully detected, vehicle presence inside the road area is hypothesized by using “shadow” as a cue. Hypothesized vehicle locations are verified using vertical edges. After extracting vehicles, the algorithm effectively tracks them using a Kalman filter based tracking algorithm. A vehicle has been instrumented with various sensors for the experiments. Several sequences from real traffic situations have been tested, obtaining highly accurate multiple vehicle detections. Tracking information is used to estimate time-to-collision (TTC) and warn the driver for a possible collision.

In this paper, a vision-based stabilization and output tracking control method for a four-rotor helicopter has been proposed. A novel 2 camera method has been described for estimating the full 6 DOF pose of the helicopter. This two camera system is consisting of a pan-tilt ground camera and an onboard camera. The pose estimation algorithm is compared in simulation to other methods (such as four point method, and a stereo method) and is shown to be less sensitive to feature detection errors on the image plane. The proposed pose estimation algorithm and non-linear control techniques have been implemented on a remote controlled quadrotor helicopter.

We present control methods for an autonomous four-rotor helicopter, called a quadrotor, using visual feedback as the primary sensor. The vision system uses aground camera to estimate the pose (position and orientation) of the helicopter. Two methods of control are studied - one using a series of mode-based, feedback linearizing controllers, and the other using a backstepping-like control law. Various simulations of the model demonstrate the implementation of feedback linearization and the backstepping controllers. Finally,. we present initial flight experiments. where the helicopter is restricted to vertical and yaw motions.