JACAR 2017 December

JACAR December 2017 academic articles

Showing 1–8 of 10 results

  • Design and Application of the Electronic Lock for Bicycle


    Abstract: A new type of the anti-theft electronic lock with the lock bolt structure is designed, which can realize the function of locking and unlocking of the bicycle safely. The hardware system includes the STC-12C5A microcontroller, RFID card, data read/write module and mechanical lock structure. The system combined the non-contact RFID card reader and the lock structure. And the static mechanical property is analyzed to meet various situations. A number of actual test results showed that the electronic lock system is safe and reliable.

  • On Autonomous Driving: Combining Holistic and Feature based Localization Systems (Reasons and Advantages)


    Abstract: This paper highlights the importance of combining intensity and feature based localization systems in autonomous driving. The intensity based localization system is represented by calculating cross correlation between LIDAR and map images. The feature based system is integrated by extracting the lateral edges from the LIDAR and map images with respect to the heading angle. An edge matching technique is then applied to estimate the lateral position based on the common extracted features. The experimental results have verified that the estimation of the lateral and longitudinal positions has been improved against the changes of weather and environmental conditions by combining the image and edge matching results.

  • Development of Inshore Fishing UAV at Sea Based on Preliminary Assessment Result


    Abstract: This paper initiates and describes the development of an inshore fishing UAV at the seashore, where the UAV can communicate with its controller by sending all the information during its flight in real time to the base station. The operation distance of the UAV from the seashore is to the fishing point will be between 500 m to 1km. The navigation method is under investigation, which is to guide the UAV from the seashore to fishing points along a predefined path planning by avoiding sudden obstacles it may face.

  • Integration of Drone’ Communication into an ITS Network


    Abstract: This paper presents the concept of a highly-sophisticated network system using unmanned aerial vehicles, further named drones, and wireless networks for enhancing safety against natural disasters. The system consists of several drones and ground vehicles. Each vehicle has a wireless network unit, which employs the dual-mode Wireless Access in Vehicular Environment (WAVE) in the 700 MHz or 5 GHz band, with the goal of creating a multi-hop (more than three hops) ad hoc network. These features enable the system to facilitate rescue and relief operations in the event of a serious disaster. The proposed system represents an innovation in the field of mesh network systems, being based on a combination of ubiquitous drones and ground vehicles.

  • LIDAR-based Object Classification for Autonomous Driving on Urban Roads


    Abstract: Object classification is an important technique for autonomous vehicles to identify surrounding dynamic objects and execute safe path planning. In this paper, a multi-class object classification method is proposed to classify objects around the vehicle into car, pedestrian bicyclist, and background using a LIDAR sensor. The various effective features of each object are computed using 3D point clouds, and Real AdaBoost algorithm is applied for multi-class classification. In addition, class probabilities are calculated and updated in a time series framework for tracking objects. Classification is evaluated using a dataset that includes long-range objects over 50m. The overall accuracy of the clusters for each frame is 92.7% and for tracking objects is 95.5%. Moreover, processing time for object classification is 0.07ms. Thus, this method can be used in real time for autonomous driving on urban roads.

  • Rapid Human Body Detection in Disaster Sites using Image Processing from Unmanned Aerial Vehicle (UAV) Cameras


    Abstract: The development and impact of technology on our everyday lives cannot be compared with the world our ancestors lived in several decades ago. This is described as the world of technology (WoT). But despite all the advancements in technologies, understanding of the mechanisms of nature and the damages caused via natural disasters, such as earthquakes, landslides, and flooding to mention only a few, are still very far away. In the effort of saving lives during natural disasters, such as earthquakes, this study introduces a rapid human body detection technology using image processing from UAV camera. The skin color from a female student is first extracted in RGB then converted to HSV. Next, opening and closing morphological operations are performed eight times each to remove all noise present in the image. Experimental tests were performed both indoor and outdoor, where the female student presented an object close and far to the camera to check the detection capability in both cases. The experiment results show that close or far, the camera can clearly detect both a human body and any part of a human body. The results of the experiment proves the merit of the proposed method.

  • Convolutional Neural Network Based Vehicle Turn Signal Recognition


    Abstract: Automated driving is an emerging technology in which a car performs the tasks of recognition, decision making, and control. Recognizing surrounding vehicles is vital in generating the trajectory of an ego-vehicle. This paper focuses on detecting a turn signal information as one of the driver’s intention for surrounding vehicles. This information helps to predict the driver’s behavior in advance especially as instances of lane change and turns at an intersection. Using their intension, the automated vehicle is able to generate a safety trajectory before the driver’s behavior changes. The proposed method recognizes the turn signal of the target vehicle using a mono-camera. It detects the lighting state using Convolutional Neural Network, and then calculates a flashing frequency using Fast Fourier Transform.

  • An Assistive Robotic Arm for People with Severe Disabilities:Evaluation of Eating Soup


    Abstract: An assistive robotic arm for people with severe disabilities is presented in this paper. Its user interface using eye movements consists of a Web camera, computer, and microcontroller with display unit. Using the robotic arm system, we performed two experiments: (1) transferring water task and (2) eating soup task. It was found from the experimental results that the assistive robotic arm system could stably and appropriately transfer more than 82 % of water and 86 % of soup to the respective positions.