Dongchul Kim

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.

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.

Signal Transient and Crosstalk Model of Capacitively and Inductively Coupled VLSI Interconnect Lines

Analytical compact form models for the signal transients and crosstalk noise of inductive- effect-prominent multi-coupled RLC lines are developed. Capacitive and inductive coupling effects are investigated and formulated in terms of the equivalent transmission line model and transmission line parameters for fundamental modes. The signal transients and crosstalk noise expressions of two coupled lines are derived by using a waveform approximation technique. It is shown that the models have excellent agreement with SPICE simulation.

Multiple exposure images based traffic light recognition

This paper proposes a multiple exposure images based traffic light recognition method. For traffic light recognition, color segmentation is widely used to detect traffic light signals; however, the color in an image is easily affected by various illuminations and leads to incorrect recognition results. In order to overcome the problem, we propose the multiple exposure technique which enhances the robustness of the color segmentation and recognition accuracy by integrating both low and normal exposure images. The technique solves the color saturation problem and reduces false positives since the low exposure image is exposed for a short time. Based on candidate regions selected from the low exposure image, the status of six three and four bulb traffic lights in a normal image are classified utilizing a support vector machine with a histogram of oriented gradients. Our algorithm was finally evaluated in various urban scenarios and the results show that the proposed method works robustly for outdoor environments.

Analytical Eye-Diagram Determination for the Efficient and Accurate Signal Integrity Verification of Single Interconnect Lines

In this paper, a new efficient and accurate analytical eye-diagram determination technique for interconnect lines is presented. The simplest input test signal model for the intersymbol interference analysis of high-speed data links is mathematically formulated. Since input test patterns for eye boundaries are determined analytically, it is considered very convenient and efficient. The proposed technique shows excellent agreement with the SPICE-based simulation in both eye height and jitter, i.e., within 5% error for nondiscontinuous data paths and 10% error for discontinuous data paths. The method is much more computation-time-efficient than the pseudorandom bit sequence-based SPICE simulation in the order of magnitude.

Compact Models for Signal Transient and Crosstalk Noise of Coupled RLC Interconnect Lines with Ramp Inputs

Analytical compact form models for the signal transient and crosstalk noise of two-coupled RLC lines are developed. Capacitive and inductive coupling effects are investigated and formulated in terms with eigen modes (i.e., even mode and odd mode). The signal transients and crosstalk noise expressions of two coupled lines are derived by using a wave-form approximation technique for the modal signals. It is shown that the models have excellent agreement with SPICE simulation.

A novel transmission line characterisation based on measurement data reconfirmation

Due to inherent resonance effects and frequency-variant dielectric properties, it is very difficult to experimentally determine the stable and accurate circuit model parameters of thin film transmission line structures over a broad frequency band. In this article, a new, simple and straightforward frequency-variant transmission line circuit model parameter determination method is presented. Experimental test patterns for high-frequency transmission line characterisations are designed and fabricated using a package process. The S-parameters for the test patterns are measured using a vector network analyzer (VNA) from 100 MHz to 26.5 GHz. The parasitic effects due to contact pads are de-embedded. The frequency-variant complex permittivity and resonance-effect-free transmission line parameters (i.e., the propagation constant and characteristic impedance) are then determined in a broad frequency band.

Nonlinear echo cancellation using an expanded correlation LMS algorithm

Nonlinear echo cancellation in a hybrid telephone network is considered, whereby adaptive Volterra filtering is utilized along with an expanded correlation LMS (ECLMS) algorithm to compensate for nonlinear distortion in the echo path. Also, some simulation results are provided to demonstrate the validity of the proposed approach even in a double-talk situation.

Overall Reviews of Autonomous Vehicle A1 - System Architecture and Algorithms

Abstract This paper describes an autonomous vehicle A1 that won the Hyundai Motor Group 2012 Autonomous Vehicle Competition. The A1 was developed for autonomous on-road and off-road driving conditions without driver intervention. The autonomous driving system consists of four parts that are localization, perception, planning, and control. Localization which estimates the ego-vehicle position on the map should be first performed to autonomously drive the A1. A perception algorithm detects and recognizes the objects around the ego-vehicle are also important to prevent collisions with obstacles and road departure. A planning algorithm generates the drivable motion of the A1 based upon previous information from the localization and perception system. Subsequently, a vehicle control algorithm calculates the desired steering, acceleration and braking control commands based on the information from the planning algorithm. This paper also presents the entire system architecture that the A1 used to accomplish all the required missions in the 2012 Autonomous Vehicle Competition.