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An estimation algorithm for vehicle driving load has been proposed in this paper. Driving load is an important factor in a vehicle's longitudinal motion control. An approach using an observer is introduced to estimate driving load based on inexpensive RPM sensors currently being used in production vehicles. Also, the new torque estimation technique using neural network has been incorporated in this estimation algorithm to achieve better performance over variations in the automotive power transmissions process. The effectiveness of the observer-based method is demonstrated through the use of a nonlinear full vehicle simulation model in various scenarios. The proposed method using an observer has good performance, both over modeling error in powertrain system and under the uncertain environment of a running vehicle.

This paper describes design and evaluation of a driving mode decision and lane change control algorithm of automated vehicle in merge situations on highway intersection. For the development of a highly automated driving control algorithm in merge situation, driving mode change from lane keeping to lane change is necessary to merge appropriately. In a merge situation, the driving objective is slightly different to general driving situation. Unlike general situation, the lane change should be completed in a limited travel distance in a merge situation. Merge mode decision is determined based on surrounding vehicles states and remained distance of merge lane. In merge mode decision algorithm, merge availability and desired merge position are decided to change lane safely and quickly. Merge availability and desired merge position are based on the safety distance that considers relative velocity and relative position of subject and surrounding vehicles.

CW/CA (Collision Warning /Collision Avoidance) Systems have been an active research and development area as interests and demands for the advanced vehicle increase. A CW/CA ‘Hardware-in-the-Loop Simulation (HiLS)’ system has been designed and used to test a CW/CA algorithm, radar sensors, and warning displays under realistic operating conditions in the laboratory. A CW/CA algorithm has two parts. One is a distance decision algorithm that determines the critical warning and braking distance and the other is a brake control algorithm for collision avoidance. The CW/CA HiLS system consists of a controller in which a DSP chip is installed, a preceding vehicle simulator, a radar sensor and a warning display. The controller calculates velocities of the preceding and following vehicles, relative distance and relative velocity of the vehicles using vehicle simulation models. The relative distance and velocity are applied to the vehicle simulator that is controlled by a DC motor.

As an effective approach for the design, implementation and test of control systems, hardware-in-the-loop (HIL) test has been used in many research areas. This paper describes a real-time HIL simulation test for an automotive electronic control system. The HIL system proposed in this paper consists of three parts: real-time target hardware, electronic control unit (ECU) of the automotive electronic control systems and a signal-conditioning unit which regulates the voltage levels between real-time target and ECU. The HIL simulation evaluates mechanical and electronic behaviors in real time using off-line simulation models by interfacing real-target with electrical control units via interface box. The model has been developed by MATLAB/Simulink. The model is composed of mechanical part which predicts dynamic behaviors and electronic part to calculate the motor speeds, current and electronic loads under the various conditions.

This paper describes a robust Model Predictive Control (MPC) framework of lane change for automated driving vehicles. In order to develop a safe lane change for automated driving, the driving mode and lane change direction are determined considering environmental information, sensor uncertainties, and collision risks. The safety margin is calculated using predicted trajectories of surround and subject vehicles. The MPC based combined steering and longitudinal acceleration control law has been designed with extended bicycle model over a finite time horizon. A reachable set of vehicle state is calculated on-line to guarantee that MPC state and input constraints are satisfied in the presence of disturbances and uncertainties. The performance of the proposed algorithm has been conducted simulation studies.

This paper presents a longitudinal control algorithm for an adaptive cruise control (ACC) with collision avoidance (CA) in multiple vehicle traffic situations. The proposed algorithm consists of a multi-target tracking filter, a primary target selection algorithm and an integrated ACC/CA system. The multi-target tracking filter is used to smooth the sensor signal, and makes it possible to apply to a control system. The primary target selection algorithm decides an in-lane target and provides the information to an integrated ACC/CA system in order to drive a subject vehicle smoothly and improve safety in complex traffic situations. Finally, the integrated ACC/CA system computes the desired acceleration. The performance and safety benefits of the multi-vehicle ACC/CA system is investigated via simulations using real data on driving. Simulation results show that the response of multi-vehicle ACC/CA system is more smooth and safer at a change of traffic situations.