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Kichun Jo,Keounyup Chu,Myoungho Sunwoo IEEE 2012 IEEE transactions on intelligent transportation sy Vol.13 No.1
<P>Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.</P>
Jo, Kichun,Jo, Yongwoo,Suhr, Jae Kyu,Jung, Ho Gi,Sunwoo, Myoungho IEEE 2015 IEEE transactions on intelligent transportation sy Vol.16 No.6
<P>This paper presents a Monte Carlo localization algorithm for an autonomous car based on an integration of multiple sensors data. The sensor system is composed of onboard motion sensors, a low-cost GPS receiver, a precise digital map, and multiple cameras. Data from the onboard motion sensors, such as yaw rate and wheel speeds, are used to predict the vehicle motion, and the GPS receiver is applied to establish the validation boundary of the ego–vehicle position. The digital map contains location information at the centimeter level about road surface markers (RSMs), such as lane markers, stop lines, and traffic sign markers. The multiple images from the front and rear mono-cameras and the around-view monitoring system are used to detect the RSM features. The localization algorithm updates the measurements by matching the RSM features from the cameras to the digital map based on a particle filter. Because the particle filter updates the measurements based on a probabilistic sensor model, the exact probabilistic modeling of sensor noise is a key factor to enhance the localization performance. To design the probabilistic noise model of the RSM features more explicitly, we analyze the results of the RSM feature detection for various real driving conditions. The proposed localization algorithm is verified and evaluated through experiments under various test scenarios and configurations. From the experimental results, we conclude that the presented localization algorithm based on the probabilistic noise model of RSM features provides sufficient accuracy and reliability for autonomous driving system applications.</P>
Generation of a Precise Roadway Map for Autonomous Cars
Kichun Jo,Myoungho Sunwoo IEEE 2014 IEEE transactions on intelligent transportation sy Vol.15 No.3
<P>This paper proposes a map generation algorithm for a precise roadway map designed for autonomous cars. The roadway map generation algorithm is composed of three steps, namely, data acquisition, data processing, and road modeling. In the data acquisition step, raw trajectory and motion data for map generation are acquired through exploration using a probe vehicle equipped with GPS and on-board sensors. The data processing step then processes the acquired trajectory and motion data into roadway geometry data. GPS trajectory data are unsuitable for direct roadway map use by autonomous cars due to signal interruptions and multipath; therefore, motion information from the on-board sensors is applied to refine the GPS trajectory data. A fixed-interval optimal smoothing theory is used for a refinement algorithm that can improve the accuracy, continuity, and reliability of road geometry data. Refined road geometry data are represented into the B-spline road model. A gradual correction algorithm is proposed to accurately represent road geometry with a reduced amount of control parameters. The developed map generation algorithm is verified and evaluated through experimental studies under various road geometry conditions. The results show that the generated roadway map is sufficiently accurate and reliable to utilize for autonomous driving.</P>
Tracking and Behavior Reasoning of Moving Vehicles Based on Roadway Geometry Constraints
Jo, Kichun,Lee, Minchul,Kim, Junsoo,Sunwoo, Myoungho IEEE 2017 IEEE transactions on intelligent transportation sy Vol.18 No.2
<P>Tracking and behavior reasoning of surrounding vehicles on a roadway are keys for the development of automated vehicles and an advanced driver assistance system (ADAS). Based on dynamic information of the surrounding vehicles from the tracking algorithm and driver intentions from the behavior reasoning algorithm, the automated vehicles and ADAS can predict possible collisions and generate safe motion to avoid accidents. This paper presents a unified vehicle tracking and behavior reasoning algorithm that can simultaneously estimate the vehicle dynamic state and driver intentions. The multiple model filter based on various behavior models was used to classify the vehicle behavior and estimate the dynamic state of surrounding vehicles. In addition, roadway geometry constraints were applied to the unified vehicle tracking and behavior reasoning algorithm in order to improve the dynamic state estimation and the behavior classification performance. The curvilinear coordinate system was constructed based on the precise map information in order to apply the roadway geometry to the tracking and behavior reasoning algorithm. The proposed algorithm was verified and evaluated through experiments under various test scenarios. From the experimental results, we concluded that the presented tracking and behavior reasoning algorithm based on the roadway geometry constraints provides sufficient accuracy and reliability for automated vehicles and ADAS applications.</P>
Kichun Jo,Keonyup Chu,Myoungho Sunwoo 한국자동차공학회 2009 한국자동차공학회 학술대회 및 전시회 Vol.2009 No.11
Global Positioning System (GPS) has been widely used for a localization system. However, the localization system based on a stand-alone GPS receiver is frequently inaccurate because of GPS outage and insufficient satellite signal. Therefore, the localization system using the stand-alone GPS has to be aided by sensor fusion technology. In this study, the vehicle localization algorithm is proposed to estimate accurate vehicle position using a vehicle model based sensor fusion algorithm. The sensor fusion algorithm is developed using a Kalman filter based on the vehicle model and GPS. In order to accurately estimate vehicle position with the Kalman filter, the vehicle model should be adapted to various driving environments. Therefore, the cornering stiffness of tires in the vehicle model is identified and adapted in real-time. The developed estimation algorithm was verified by simulation using a commercial vehicle model. The simulation results show that the estimation accuracy of the developed algorithm is accurate enough to be implemented in a vehicle for various driving conditions.
Development of Autonomous Car—Part I: Distributed System Architecture and Development Process
Kichun Jo,Junsoo Kim,Dongchul Kim,Chulhoon Jang,Myoungho Sunwoo Institute of Electrical and Electronics Engineers 2014 IEEE transactions on industrial electronics Vol. No.
<P>An autonomous car is a self-driving vehicle that has the capability to perceive the surrounding environment and navigate itself without human intervention. For autonomous driving, complex autonomous driving algorithms, including perception, localization, planning, and control, are required with many heterogeneous sensors, actuators, and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, this paper applies distributed system architecture to the autonomous driving system, and proposes a development process and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. For the heterogeneous computing system of the distributed system, a system platform is presented, which provides a common development environment by minimizing the dependence between the software and the computing hardware. A time-triggered network protocol, FlexRay, is applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance. Part II of this paper will provide the evaluation of the development process and system platform by using an autonomous car, which has the ability to drive in an urban area.</P>