Kichun Jo

Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

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.

Development of Autonomous Car—Part I: Distributed System Architecture and Development Process

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.

Development of Autonomous Car—Part II: A Case Study on the Implementation of an Autonomous Driving System Based on Distributed Architecture

Part I of this paper proposed a development process and a system platform for the development of autonomous cars based on a distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction in computational complexity, fault-tolerant characteristics, and system modularity. In this paper (Part II), a case study of the proposed development methodology is addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed.

GPS-bias correction for precise localization of autonomous vehicles

This paper presents a precise localization method for autonomous driving systems by correcting the GPS bias error. Since GPS errors have systematic noise properties that change slowly with time, a stand-alone GPS cannot be used for localization of an autonomous vehicle. To compensate for this systematic bias error, several types of additional sources of information, including on-board motion sensors, camera vision systems, and a road map database, are applied to the localization system. The localization algorithm is based on a particle filter, because the measurement model related to the representation of the road geometry is described by a highly nonlinear function. The proposed localization algorithm was tested and verified through an autonomous driving test.

Integration of multiple vehicle models with an IMM filter for vehicle localization

A vehicle localization system can be extremely useful for intelligent transformation systems (ITS) such as advanced driver assistance systems (ADASs), emergency vehicle notification systems, and collision avoidance systems. To optimize the performance of vehicle localization systems, localization algorithms that analyze multi-sensor data processed using a Kalman filter have been developed. However, a Kalman filter with a single process model cannot guarantee the accuracy of localization under various driving conditions, because the single vehicle model does not cover all driving situations. Therefore, we present a position estimation algorithm based on an interacting multiple model (IMM) filter that uses two kinds of vehicle models: a kinematic vehicle model and a dynamic vehicle model. While the kinematic vehicle model is suitable for low-speed and low-slip driving conditions, the dynamic vehicle model is more appropriate for high-speed and high-slip situations. The IMM filter integrates the estimates from a kinematic vehicle model based on an extended Kalman filter (EKF) and estimates from a dynamic vehicle model based on EKF to improve localization accuracy. The developed estimation algorithm was verified by simulation using a commercial vehicle model. The simulation results show that the estimates of vehicle position by the algorithm presented in this study are accurate under a wide range of driving conditions.

Generation of a Precise Roadway Map for Autonomous Cars

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.

Real-Time Road-Slope Estimation Based on Integration of Onboard Sensors With GPS Using an IMMPDA Filter

This paper proposes a road-slope estimation algorithm to improve the performance and efficiency of intelligent vehicles. The algorithm integrates three types of road-slope measurements from a GPS receiver, automotive onboard sensors, and a longitudinal vehicle model. The measurement integration is achieved through a probabilistic data association filter (PDAF) that combines multiple measurements into a single measurement update by assigning statistical probability to each measurement and by removing faulty measurement via the false-alarm function of the PDAF. In addition to the PDAF, an interacting multiple-model filter (IMMF) approach is applied to the slope estimation algorithm to allow adaptation to various slope conditions. The model set of the IMMF is composed of a constant-slope road model (CSRM) and a constant-rate slope road model (CRSRM). The CSRM assumes that the slope of the road is always constant, and the CRSRM assumes that the slope of the road changes at a constant rate. The IMMF adapts the road-slope model to the driving conditions. The developed algorithm is verified and evaluated through experimental and case studies using a real-time embedded system. The results show that the performance and efficiency of the road-slope estimation algorithm is accurate and reliable enough for intelligent vehicle applications.

Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras

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.

Distributed vehicle state estimation system using information fusion of GPS and in-vehicle sensors for vehicle localization

This paper proposes a distributed vehicle state estimation system to improve the performance of vehicle positioning using Global Positioning System (GPS) and in-vehicle sensor components. The distributed architecture of the estimation system can reduce the computational complexity of high-order estimation by dividing it into several small-order estimation modules, and simplifies fault detection and isolation problems. The distributed vehicle state estimation algorithm consists of three estimation modules. The first is a longitudinal vehicle state estimation module which estimates the longitudinal vehicle speed and road slope. The road slope estimate is used to compensate for the vertical speed on the sloped road. The second module is a lateral vehicle state estimator which estimates yaw rate, yaw, and side slip angle using an Interacting Multiple Model (IMM) filter. The last is a position estimation module which integrates the vehicle states from the previous two modules with GPS data to obtain more accurate position information. The proposed estimation algorithm was verified through simulation with the aid of a commercial vehicle model. The results demonstrate the efficiency and accuracy of the proposed algorithm.

Road Slope Aided Vehicle Position Estimation System Based on Sensor Fusion of GPS and Automotive Onboard Sensors

This paper proposes a road slope aided position estimation algorithm based on the fusion of GPS data with information from automotive onboard sensors. Many previous studies for position estimation did not consider the effect of road slope, although many sloped roads are existing. In order to analyze the influence of road slope on position estimation, theoretical proof and simulation are performed. Based on this analysis, a road slope aided position estimation algorithm is presented, which includes a vehicle motion model that can compensate for the effect of the road slope. This algorithm can estimate the position and road slope simultaneously. Furthermore, by compensating for the error due to the road slope, the algorithm can improve the position estimation accuracy and reliability. The estimation algorithm in this paper is implemented and evaluated using automotive onboard sensors and embedded system; therefore, additional motion sensors and high-performance computational units are not necessary. The experimental results show that the accuracy and reliability of the road slope aided position algorithm provide superior performance compared with a planar vehicle model-based position estimation algorithm in mountainous terrain.