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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 evidential theory is widely used for 2D grid-based localization in a robotics field because the theory has benefits to consider additional states such as 'unknown' and 'conflict'. However, there are some problems such as computational limitation and excessive resource share when the localization system is expanded from 2D grid to 3D voxel. In order to overcome the problems, this paper proposes the parallelized particle filter based localization system using 3D evidential voxel maps. A many-core processor based parallel computing framework with optimization techniques is applied to accelerate the computing power. Experiments were performed to evaluate the performance of the localization system in a complex environment, and to compare the computational time and resources between various types of processing units. The experimental results show that the proposed parallel particle filter is much more efficient than particle filter without parallel computing regarding computational cost.

With fuel efficiency becoming an increasingly critical aspect of internal combustion engine (ICE) vehicles, the necessity for research on efficient generation of electric energy has been growing. An energy management (EM) system controls the generation of electric energy using an alternator. This paper presents a strategy for the EM using a control mode switch (CMS) of the alternator for the (ICE) vehicles. This EM recovers the vehicle’s residual kinetic energy to improve the fuel efficiency. The residual kinetic energy occurs when a driver manipulates a vehicle to decelerate. The residual energy is commonly wasted as heat energy of the brake. In such circumstances, the wasted energy can be converted to electric energy by operating an alternator. This conversion can reduce additional fuel consumption. For extended application of the energy conversion, the future duration time of the residual power is exploited. The duration time is derived from the vehicle’s future speed profile.

Parking path planning is an essential technology for intelligent vehicles. Under a confined area, a parking path has to guide a vehicle into a parking space without collision. To realize this technology, circle-based planning algorithms have been studied. The main components of these algorithms are circles and straight lines; subsequently, the parking path of the algorithm is designed by the combination of these geometric lines. However, the circle-based algorithm was developed in an open space within an unlimited parking lot width, so a feasible path cannot always be guaranteed in a narrow parking lot. Therefore, we present a parking planning algorithm based on Turning Standard Line (TSL) that is a straight line segment. The algorithm uses the TSL lines to guide sequential quadratic Béizer curves. A set of these curves from parking start to goal position creates a continuous parking path.

In this paper, we proposed an estimation model for the Indicated Mean Effective Pressure (IMEP) which means the generated torque by the combustion with using only a single cylinder pressure sensor. The IMEP of each cylinder leads the rotational crankshaft acceleration. Based on the rotational dynamics, we can determine an empirical model structure between IMEP and crankshaft acceleration to estimate the IMEP of an individual cylinder. The proposed model calculates the IMEP of an individual cylinder by applying the IMEP obtained from a single pressure sensor and crankshaft acceleration. Consequently, the proposed estimation method can be used for combustion control with cost affordable equipment. In addition, it is beneficial for a real-time system because the calculation time of IMEP of other cylinders can be eliminated. The proposed model is validated through the engine experiment.

This paper presents an exhaust gas recirculation (EGR) rate estimation algorithm for a turbocharged diesel engine. Accurate estimation of the EGR rate is important for precise air system control of a diesel engine. In order to estimate the EGR rate accurately, we developed an adaptive observer using a model reference identification scheme (MRIS). A linear parameter varying model for the intake manifold pressure dynamics is derived as the reference model for the adaptive observer. The intake and exhaust temperature models are developed through an empirical approach. The MRIS is used to design an update rule for the adaptive observer. Convergence of the proposed observer is proven by using the Lyapunov stability criterion. The proposed observer is implemented in a real-time embedded system and validated via engine experiments.

The development of autonomous vehicle requires the state-of-the-art technologies in perception, planning, control, and system integration. This paper presents an overview of the system architecture and software architecture of autonomous vehicles for system integration. Network based system architecture in this paper provides a distributed computing system for autonomous driving. Further, a real-time path planning and a target speed generation are described based on the curvilinear coordinate system. The design of a path in the curvilinear coordinate system stretches the design space as like the Cartesian coordinate system to simplify the generation of the path. In determination of target speed, curvatures and risk of a generated path were utilized for safe autonomous driving.

Cylinder air charge is an important parameter to reduce generation of visible emissions by adjusting the amount of fuel injected into a diesel engine. In this study, we propose a cylinder air charge estimation algorithm for a diesel engine equipped with variable geometry turbocharger (VGT), exhaust gas recirculation (EGR), and swirl control valve (SCV). The estimation algorithm predicts the cylinder air charge using a mean value air path model and measurable signals available in mass produced engines. The estimation algorithm addresses effects of the VGT, EGR, and SCV on the cylinder air charge. The proposed estimation algorithm was validated with a 1-D engine model simulation.

In a multi-cylinder variable valve lift (VVL) engine, in spite of its high efficiency and low emission performance, operation of the variable valve lift brings about not only variation of the air-fuel ratio at the exhaust manifold, but also individual cylinder air-fuel ratio maldistribution. In this study, in order to reduce the air-fuel ratio variation and maldistribution, we propose an individual cylinder air-fuel ratio estimation algorithm for individual cylinder air-fuel ratio control. For the purpose of the individual cylinder air-fuel ratio estimation, air charging dynamics are modeled according to valve lift conditions. In addition, based on the air charging model, individual cylinder air-fuel ratios are estimated by multi-rate sampling from single universal exhaust gas oxygen (UEGO) sensor located on the exhaust manifold. Estimation results are validated with a one-dimensional engine simulation tool.

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.

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.

Electromechanical relays that are coupled with fuses have been used for controlling electrical loads in vehicles. In the past decade, semiconductor power switches have been developed for overcoming the physical limits of relays and fuses. Semiconductor power switches can not only replace relays and fuses but can also improve a system's reliability and efficiency. In this study, we introduce the Smart Automotive Switch (SAS), which is a smart high side power switch of Fairchild Korea semiconductor. Functional capabilities, such as power switching, protection and self-diagnosis of SASs are presented in case studies involving, for example, headlights, glow plugs, and fuel pump control systems. Through these experimental studies, the suitability of SASs is validated for designing improved automotive electronic control systems.

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.

In CRDI diesel engines, the piezo injector is gradually replacing the solenoid injector due to the quick response of the actuator. Operating performance of the injectors in the CRDI diesel engine has an influence on engine emissions. Therefore, accurate injector control is one of the most important parts of the CRDI engine control. The objective of this paper is the development of a piezo injector driver for CRDI diesel engines. Electrical characteristics of the piezo injector were analyzed. A control strategy for charging and discharging the actuator are proposed. The developed injector driver is verified by experiments under various fuel pressures, injection durations and driving circuit voltages.

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.

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.

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.