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A Light Detection And Ranging (LiDAR) is now becoming an essential sensor for an autonomous vehicle. The LiDAR provides the surrounding environment information of the vehicle in the form of a point cloud. A decision-making system of the autonomous car is able to determine a safe and comfort maneuver by utilizing the detected LiDAR point cloud. The LiDAR points on the cloud are classified as dynamic or static class depending on the movement of the object being detected. If the movement class (dynamic or static) of detected points can be provided by LiDAR, the decision-making system is able to plan the appropriate motion of the autonomous vehicle according to the movement of the object. This paper proposes a real-time process to segment the motion states of LiDAR points. The basic principle of the classification algorithm is to classify the point-wise movement of a target point cloud through the other point clouds and sensor poses.

Intelligent driving assistant systems have been studied meticulously for autonomous driving. When the systems have the responsibility for driving itself, such as in an autonomous driving system, it should be aware of its’ surroundings including moving vehicles and must be able to evaluate collision risk for the ego vehicle's planned motion. However, when recognizing surrounding vehicles using a sensor, the measured information has uncertainty because of many reasons, such as noise and resolution. Many previous studies evaluated the collision risk based on the probabilistic theorem which the noise is modeled as a probability density function. However, the previous probabilistic solutions could not assess the collision risk and predict the motion of surrounding vehicles at the same time even though the motion is possible to be changed by the estimated collision risk.

An efficient development environments such as Software-in-the-Loop Simulation (SILS) and Rapid Control Prototyping (RCP) have been widely used to reduce the development time and cost of real-time ECUs. However, conventional SILS does not consider temporal behaviors caused by computation time, task scheduling, network-induced delays, and so on. As a result, the control performance of ECU is likely to be degraded after implementation. To overcome this problem, SILS/RCP which considers the temporal behaviors was suggested in the previous research. In this study, we validated the proposed SILS/RCP environments which are used to design an Electronic Stability Control (ESC) system which is one of the hard real-time control systems. The proposed SILS/RCP environments make it possible to realize ECUs in the early design phase by considering temporal behaviors.

FlexRay is a deterministic and fault-tolerant in-vehicle network(IVN) protocol. It is expected to become a practical standard for automotive communication systems. According to the FlexRay protocol specifications, there are about 60 configurable parameters which should be determined in the design phases. The parameters increase the complexities of FlexRay-based control system development. In this study, we are suggesting a formal design process for FlexRay-based control systems, which is focused on network parameter optimization. We introduce design phases from functional system models to implementations. These phases present formal ways for task allocation, node assignment, network configuration, and implementations. In the network configuration phase, two FlexRay core parameters are selected to optimize network design. Optimal methods of the core parameters provide concise guide lines for optimal communication cycle length and optimal static slot length.

Controlled Auto-Ignition (CAI) combustion is a new combustion concept. Unlike the conventional internal combustion engine, CAI combustion takes place homogeneously throughout the fuel/air mixture with self ignition, and the mixture is burned without flame propagation. The start of combustion (SOC) is a critical factor in the combustion because SOC affects exhaust gas emissions, engine power, fuel economy and combustion characteristics. This paper presents a control oriented SOC detection method using a 10 bar of difference pressure, and proposes 50 percent normalized difference pressure for SOC detection parameter. Difference pressure is defined as the difference between the in-cylinder firing pressure and the in-cylinder motoring pressure. These methods were determined by CAI combustion experiments. Managing the difference pressure is a fast and precise method for SOC detection.

The successful monitoring of the combustion process depends on the accurate measurement of in-cylinder pressure. Piezoelectric transducers are normally used for in-cylinder pressure measurement. However, rapid changes in the temperature of the transducer housing and the quartz sensing element can change the transducer offset voltage. Therefore, piezoelectric transducers require referencing the output to the absolute pressure (pegging). This study reviews several pegging methods and proposes a modified method based on a variable polytropic coefficient. The feasibility of the proposed method was validated using both the simulated and the experimental pressure data from a common-rail direct injection (CRDI) diesel engine.

A fault detection method with parity equations is proposed in this paper. Due to low cost implementation, the velocity of a motor is not measurable in EPB systems. Therefore, residuals are not reliable with a low resolution encoder to estimate the motor velocity. In this paper, we propose a fault detection method with sensorless estimation using current ripples. The method estimates position and velocity of the motor by detecting periodical oscillations of the armature current caused by rotor slots. This method could estimate position and velocity of the motor with less computational effort than a state observer. Moreover, the method is less sensitive to motor parameters than model-based estimation methods. The effectiveness of this method is validated with experimental data. The simulation results show that various faults have their own residual patterns. Therefore, we could detect the fault by monitoring the residual signals.

This paper presents three types of controllers for Electric Parking Brake (EPB) Systems: bang-bang, linear proportional (P), and nonlinear proportional (P) controller. Mechanical and electrical parts of EPB system are modeled and implemented using Modelica language. There is good agreement between simulation and experimental results. For the stability analysis, the EPB system is modeled as a state-dependent switched system with simplified friction dynamics. From simulation and experimental results, it turns out that the nonlinear P controller provides good uniformity in performance and robustness among them.

This paper presents a start of combustion (SOC) control for a common rail direct injection (CRDI) diesel engine, which is achieved by utilizing in-cylinder pressure signals. The difference pressure (DP), which is the difference between the in-cylinder firing pressure and motoring pressure, is selected as the variable for SOC detection. An adaptive feedforward controller was applied in order to improve the performance of the feedback controller. The feedforward controller consists of the radial basis function network (RBFN) and the feedback error learning method that is for training of the network. In this paper, the RBFN has two inputs which are engine speed and target SOC, and has one output, start of energizing. The feasibility and performance of the proposed controller were validated by transient engine operation experiments.

In this paper, we proposed a formalized design procedure for networked control systems (NCSs). In a conventional development of NCSs, well-designed control algorithms do not result in the intended control performance after an implementation due to time delays, such as network-induced delays and controller computation delays. The proposed design procedure shows how to minimize the degradation of the control performance caused by the time delays. The design procedure was verified by designing a network-based traction control system (TCS). The designed TCS was realized and tested by using a rapid control prototyping (RCP) platform and a hardware-in-the-loop simulation (HILS) environment.

Lane keeping assistant system (LKAS) is expected to reduce the driver workload with assisting the driver during driving and is regarded as a promising active safety system. For the proposed LKAS which requires cooperative driving between driver and the assistance system, a Model Based Predictive Controller (MBPC) is proposed to minimize the effect of system overshoot caused by the time delay from the vision-based lane detection system. In order to validate the proposed LKAS controller, a HIL (Hardware In the Loop) simulator is built using steering mechanism, single camera, torque motor, sensors, etc. The performance of the proposed system is demonstrated in various roadways.

A computer aided test system, which is called MOde Survey System (MOSS), is newly developed to evaluate a driving pattern and traffic flow in Seoul, Korea. This system is designed for the people who work on vehicle emissions and energy more quantitatively with the aim of being cost-effective and easy-to-use. Compared to currently-existing Test and Measurement Systems, MOSS is designed and developed for a specific goal with a couple of unique features. These features are: 1) To be able to be used as either a stand alone system like a data logging system or a real-time processing system with a PC to easily visualize vehicle performances and traffic information during the test. 2) To provide a statistical analysis program for easy use to analyze of driving pattern and traffic situation with logged test data files.

A control method using an in-cylinder pressure sensor can directly and precisely control engine combustion, lowering harmful emissions and fuel consumption levels. However, this method cannot be applied to a conventional engine management system because of its inaccuracy and the high cost of the pressure sensor, as well as the high computational load. In this study, we propose a real-time IMEP estimation method for a common rail direct injection diesel engine using the difference pressure integral as a cylinder pressure variable. The proposed method requires less computational load, enabling the IMEP to be estimated in real-time. In addition, we validated the estimation algorithm through simulation and engine experiments. The IMEP was accurately estimated with a small root mean square error of below 0.2305 bar.

In this paper, a nonlinear dynamic engine model is introduced, which is developed to represent an SI engine over a wide range of operating conditions. The model includes intake manifold dynamics, fuel film dynamics, and engine rotational dynamics with transport delays inherent in the four-stroke engine cycles, and can be used for designing engine controllers. The model is validated with engine-dynamometer experimental data. The accuracy of the model is evaluated by the comparison of the simulated and the measured data obtained from a 2.0 L inline four-cylinder engine over wide operating ranges. The test data are obtained from 42 operating conditions of the engine. The speed range is from 1500 (rpm) to 4000 (rpm), and the load range is from 0.4 (bar) to WOT. The results show that the simulation data from the model and the measured data during the engine test are in good agreement.

Since many electric and electronic systems are continuously added in a vehicle to meet various regulations and customer demands over the last decade, the demand on the electric power have been substantially increased. Furthermore the idle time fraction during the vehicle traveling has been increased due to the heavy urban traffic condition. The electric power system of the modern vehicle has to supply enough electrical energy to numerous electrical and electronic systems. A detailed understanding of the characteristics of the electric power system, electrical load demands, and driving environment such as road, season, and vehicle weight are required when the capacities of generator and battery are determined for a vehicle. In order to avoid an over or under design problem of the electric power system, a simulation program for electric power estimation is adequate.

This paper presents the Matlab/Simulink-based Software-in-the-Loop Simulation (SILS) tool which is the co-simulator for temporal and functional simulations of control systems. The temporal behavior of a control system is mainly dependent on the implemented software and hardware such as the real-time operating system, target CPU and communication protocol. In this research, the SILS components with temporal attributes are specified as tasks, task executions, real-time schedulers, and real-time networks. Methods for realizing these components in graphical block representations are investigated with Matlab/Simulink, which is the most commonly used tool for designing and simulating control algorithms in control engineering. These components are modeled in graphical blocks of Matlab/Simulink.

Computer Aided Control System Design (CACSD) tools are widely used in the development of embedded control systems. Automatic code generation for CACSD models is the subject of increasing interest. In this study, Software-in-the-Loop Simulation (SILS) and Rapid Control Prototyping (RCP) are proposed as a development framework for the design of real-time control systems. SILS is a simulation environment to consider functional behavior as well as temporal behavior of control systems. RCP supports seamless development from design to implementation through automatic code generation. SILS/RCP environments make it possible to design and analysis control systems under conditions similar to real execution during off-line simulation and to realize controllers in the early design phase.

In this paper, a control oriented cylinder-by-cylinder engine model (CCEM) and ECU-in-the-loop simulation (EILS) of common-rail direct injection (CRDI) diesel engine are presented. The CCEM includes the combustion model of torque production so that it is possible to acquire the in-cycle information, such as cylinder pressure. EILS environment using the CCEM is proposed for cylinder pressure based controller design. It allows real-time engine simulation available, and is applicable for developing the control logic and validating prototype ECUs. Finally, the accuracy of the CCEM is evaluated by the engine experimental data.

Increasing demands on the emission reduction of high speed direct injection (HSDI) diesel engines require more accurate control of injection parameters such as the injection timing, injection rate, and injection quantity. In order to meet injection requirements, the piezo injector, which has a piezoelectric element as an actuator, has been recently developed. Compared with solenoid-actuated injectors, piezo-actuated injectors yield greater force and give faster response times, resulting in more accurate and faster injections. In this study, a mathematical model of a piezo-actuated injector is developed. The injector model consists of three subsystems: the piezo-actuator subsystem, the mechanical subsystem, and the hydraulic subsystem. The constitutive relations of piezoelectricity are used for modeling the piezo-actuator subsystem. An estimation method of the injection timing and rate is introduced based on the proposed model.

This paper proposes a control and fault diagnosis method for a pressure sensor based brake control system. The proposed wheel brake pressure control method consists of feedforward and feedback controller, respectively. The main purpose of the feedforward controller is to set the operating point of the feedback control, and the purpose of feedback controller is to improve the control response and the steady state error characteristic. Also, the proposed fault diagnosis method consists of three processes: a fault detection process, a fault isolation process and a fault identification process. In the fault detection process, a fault is detected by the difference between the estimated signal and the measured signal. Then, in the fault isolation process, the location of the fault is determined. Finally, in the identification process, the size and effect of the fault are evaluated.