Junsoo Kim

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.

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.

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.

Real-Time IMEP Estimation and Control Using an In-Cylinder Pressure Sensor for a Common-Rail Direct Injection Diesel Engine

An in-cylinder pressure-based control method is capable of improving engine performance, as well as reducing harmful emissions. However, this method is difficult to be implemented in a conventional engine management system due to the excessive data acquisition and long computation time. In this study, we propose a real-time indicated mean effective pressure (IMEP) estimation method using cylinder pressure in a common-rail direct injection diesel engine. In this method, difference pressure integral (DPI) was applied to the estimation. The DPI requires only 180 pressure data points during one engine cycle from top dead center to bottom dead center when pressure data are captured at every crank angle. Therefore, the IMEP can be estimated in real time. To further reduce the computational load, the IMEP was also estimated using DPI at 2 deg, 3 deg, and 4 deg crank angle resolutions. Furthermore, based on the estimated IMEP, we controlled IMEP using a radial basis function network and linear feedback controller. As a result of the study, successful estimation and control were demonstrated through engine experiments.

Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles

This paper presents a novel curvilinear-coordinate-based approach to improve object and situation assessment performance for highly automated vehicles under various curved road conditions. The approach integrates object information from radars and lane information from a camera with three steps: track-to-track fusion, curvilinear coordinate conversion, and lane assessment. The track-to-track fusion is achieved through a nearest neighbor filter that updates the target state estimation and covariance with the nearest neighbor measurement, and a cross-covariance method that merges the duplicate tracks using error covariance. In order to determine in which lane the fused tracks are located accurately and reliably, the curvilinear coordinate conversion process is performed. The curvilinear coordinates are generated in the form of a cubic Hermite spline lane model from the lane information of the camera. Based on the converted track information and the lane model in the curvilinear coordinates, the probability distribution of the threat levels in each lane is determined though a probabilistic lane association and threat assessment. The developed algorithm is verified and evaluated through experiments using a real-time embedded system. The results show that the proposed curvilinear-coordinate-based approach provides excellent performance of object and situation assessment, in respect of accuracy and computational efficiency, in real-time operation.

Road-model-based and graph-structure-based hierarchical path-planning approach for autonomous vehicles

This paper presents a path-planning strategy for autonomous vehicles which aims to provide safe and feasible manoeuvres in various driving environments. Our strategy uses a hierarchical architecture which consists of three components: a behaviour planner, a map and path selector and a local-path planner. The behaviour planner performs the rule-based decision process which determines the overall vehicle manoeuvres. The map and path selector preprocesses perception data and chooses a local-path-planning algorithm using the results of the behaviour planner. From this selection, the local-path planner generates a driveable and collision-free path. For reliable path generation under various driving conditions, the proposed local path planner employs two algorithms: a road-model-based path planning algorithm and a graph-structure-based path-planning algorithm. The former is used for structured road driving, such as lane keeping or changing, and the latter is used for unstructured road driving. The proposed hierarchical path-planning algorithm was implemented in the autonomous vehicle called A1, which was applied with an in-vehicle-network-based distributed system architecture. A1 won the 2012 Autonomous Vehicle Competition organized by the Hyundai Motor Group in Korea.

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.

Behavior and Path Planning Algorithm of Autonomous Vehicle A1 in Structured Environments

Abstract This paper presents a behavior and path planning algorithm that is responsible for safe autonomous driving in structured environments. In order to accomplish safe driving, the mission planner makes a decision of vehicle control mode by using a road map and perception information. Based on the vehicle control mode, the path planner generates the optimal path to reach the destination without collision or traffic accidents. The control strategy of acceleration and deceleration of the vehicle is determined for accurate mission completion by a longitudinal motion planner. The proposed planning algorithms were implemented on the autonomous vehicle A1 that won the 2012 Autonomous Vehicle Competition (AVC) organized by the Hyundai Motor Group in Korea.

Turning Standard Line (TSL) Based Path Planning Algorithm for Narrow Parking Lots

This work was supported by the National Research Foundation (NRF) grant funded by the Korea government (MEST) (No. 2011-0017495), Industrial Strategy Technology Development Program of Ministry of Knowledge Economy (No. 10039673 and No. 10042633), Energy Resource R&D program (2006ETR11P091C) under the Ministry of Knowledge Economy. And it was also financially supported by the BK21 plus program (22A20130000045) under the Ministry of Education, Republic of Korea.