Myoungho Sunwoo

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.

Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles

In this paper, a real-time path-planning algorithm that provides an optimal path for off-road autonomous driving with static obstacles avoidance is presented. The proposed planning algorithm computes a path based on a set of predefined waypoints. The predefined waypoints provide the base frame of a curvilinear coordinate system to generate path candidates for autonomous vehicle path planning. Each candidate is converted to a Cartesian coordinate system and evaluated using obstacle data. To select the optimal path, the priority of each path is determined by considering the path safety cost, path smoothness, and path consistency. The proposed path-planning algorithms were applied to the autonomous vehicle A1, which won the 2010 Autonomous Vehicle Competition organized by the Hyundai-Kia Automotive Group in Korea.

Enhanced Road Boundary and Obstacle Detection Using a Downward-Looking LIDAR Sensor

Detection of road boundaries and obstacles is essential for autonomous vehicle navigation. In this paper, we propose a road boundary and obstacle detection method using a downward-looking light detection and ranging sensor. This method extracts line segments from the raw data of the sensor in polar coordinates. After that, the line segments are classified into road and obstacle segments. To enhance the classification performance, the estimated roll and pitch angles of the sensor relative to the scanning road surface in the previous time step are then used. The classified road line segments are applied to track the road boundaries, roll, and pitch angles by using an integrated probabilistic data association filter. The proposed method was evaluated with the autonomous vehicle A1, which was the winner of the 2010 Autonomous Vehicle Competition in Korea organized by the Hyundai-Kia automotive group. The proposed method using the estimated roll and pitch angles can detect road boundaries and roadside, as well as road obstacles under various road conditions, including paved and unpaved roads and intersections.

FlexRay Network Parameter Optimization Method for Automotive Applications

This paper describes a new FlexRay network parameter optimization (NPO) method consisting of two straightforward steps: design the lengths of the static (ST) slot and the communication cycle. In the first step, we formulate an optimal problem for designing the ST slot length. The bandwidth limitation, composed of protocol overhead and unused network resources, is considered as optimal criterion for efficient network usage. With the exploration of design space (i.e., ST slot lengths which satisfy the constraints of the FlexRay specifications), the optimal ST slot length is determined. Based on the result of the first step, the optimal communication-cycle length is designed in the next step. In this step, we proposed an algorithm for analyzing the worst case response times (WCRTs) for the ST and dynamic frames. Using this algorithm, the optimal communication-cycle length with minimum network delays is derived. The NPO method formalizes the FlexRay network configuration process in two steps for simplicity. Furthermore, the method enables the efficient usage of FlexRay bus with minimum WCRT. Finally, vehicle chassis control system based on FlexRay network is designed using the proposed NPO method. In this example system, we verified the parameter-optimization algorithm, and analysis results are confirmed.

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.

AUTOSAR-ready Light Software Architecture for Automotive Embedded Control Systems

This paper presents AUTOSAR-ready light software architecture (AUTOSAR-Lite), which is a light weighted version of the AUTOSAR, for automotive embedded control systems. The proposed AUTOSAR-Lite reduces overhead problems caused by the excessive standard specifications of AUTOSAR. Concurrently, AUTOSAR-Lite keeps advantages of AUTOSAR such as a scalability, re-usability, reliability, and transferability. The fundamental design of AUTOSAR-Lite is originated from the AUTOSAR standard. AUTOSAR-Lite is composed of three layers such as an application software, runtime environment, and basic software layer. The application software layer adopts component-based design methodology as AUTOSAR. The runtime environment layer integrates interfaces between application and basic software layers. In case of the basic software layer, restrictions of the module configurations and interfaces of basic software are minimized. In order to validate the feasibility of AUTOSAR-Lite, a software design result based on AUTOSAR-Lite software architecture for electronic throttle control (ETC) system is suggested.

Application Software Modeling and Integration Methodology using AUTOSAR-ready Light Software Architecture

This paper describes a model-based software development methodology for AUTOSAR-ready light software architecture(AUTOSAR-Lite). The proposed methodology briefly represents an application software modeling technique using Matlab/Simulink. Using the proposed technique, application software architecture elements (e.g. software components, runnables, and interfaces) and functional behaviors can be designed in a single modeling environment. From the designed model, the codes of application software is automatically generated using Real-Time Workshop Embedded Coder. The generated application software is easily integrated with hand-coded basic software using the proposed method. In order to evaluate the proposed methodology, a diesel engine management system for a passenger car was employed as a case study. Based on the methodology, 8 atomic software components and 52 runnables are successfully developed, and they are evaluated by engine experiments. From this case study, AUTOSAR compatible model-based application software was successfully developed, and the effectiveness of the proposed methodology was evaluated.

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.