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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.

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.