Youngseop Son

Multirate active steering control for autonomous vehicle lateral maneuvering

A multirate steering control scheme is developed for autonomous vehicle lateral maneuvering. The proposed scheme consists of a multirate extended Kalman filter and a state feedback control. The multirate extended Kalman filter is to estimate the vehicle states at a fast rate of the car ECU using multirate sensing - a slow vision-based lane detection by a camera and fast motion detection by inertia sensors. A method to design the multirate decentralized extended Kalman filter is presented. Through application results, the proposed multirate steering control scheme is shown to exhibit significantly improved control performance.

Stability Analysis of an Electric Parking Brake (EPB) System with a Nonlinear Proportional Controller

In this paper, an Electric Parking Brake (EPB) system is modelled as a state-dependent switched system. The model involves screw friction which varies depending on the operation region. A new nonlinear proportional (P) controller is proposed and its stability is analyzed via Lyapunov and LaSalles theory. It is shown that the equilibrium point is locally uniform and ultimately bounded.

Adaptive side slip angle observer using simple combined vehicle dynamics

In this paper, we propose a new adaptive side slip observer using a simple combined vehicle dynamics. Though there are many ways to estimate the side slip angle, some problems still exist in the popular estimation techniques based on the kinematic model, the dynamic model, and the combined kinematic-dynamic model. Therefore we propose the adaptive side slip observer. A combined model of linearized roll motion and lateral vehicle dynamics was used to involve the roll motion in the second-order lateral vehicle dynamics. Furthermore, a time delay was also applied to the combined model, reflecting the reaction time between the steering angle and the side slip angle. We designed an H∞ discrete-time slip angle observer using linear matrix inequality to reduce the infinite norm between a set of disturbance inputs and the estimation error. The performance of the proposed method was validated via simulations and experiments.

Slip Angle Estimation: Development and Experimental Evaluation

Abstract This paper develops a slip angle estimation method using measurements of lateral acceleration and yaw rate. A unique model for slip angle estimation is developed by combining velocity kinematics, yaw dynamics, and lateral translational dynamics, so as to avoid sensor drift and parameter variation effect. The model is used in designing a state estimator to estimate slip angle, which compensates slip angle prediction error using the measurements of lateral acceleration and yaw rate. Experimental performance verification of the proposed slip angle estimation scheme is presented.

Time delay compensation for environmental sensors of high-level automated driving systems

This paper presents a time delay compensation algorithm for environmental sensors of automated driving systems. The time delay involved with the transmission of the measurements from the sensors to the processor cannot be negligible because it is responsible for estimation and control of the system. As the automotive environmental sensors such as laser scanner or radar perform measurements at a constant frequency, the measurement time latencies can be assumed to be constant. From this aspect, the constant time delay characteristics is analyzed via vehicle tests and compensated by forward estimation based coordinate transformation. The proposed compensation algorithm has been verified via test data based open loop simulation of Automated Driving Systems (ADS). It is shown that the proposed compensation enhances environment perception performance and drivers safety.

Probabilistic threat assessment with environment description and rule-based multi-traffic prediction for integrated risk management system

The objective of this paper is to propose an original probabilistic threat assessment method to completely predict and avoid all possible kinds of collision in multi-vehicle traffics. The main concerns in risk assessment can be summarized as three requirements: 1) a description of a traffic situation containing the geometric description of the road, dynamic and static obstacle tracking, 2) a prediction of multiple traffics reachable set under the reasonable behavior restriction, and 3) an assessment of collision risk which corresponds with driver sensitivity and can be applied to many complex situations without loss of generality. To fulfill these three requirements, the proposed algorithm for estimating the probability of collision occurrence of the ego vehicle follows the basic idea of the particle filtering and the collision probability can be numerically implemented and calculated. The overall performance of the proposed threat assessment algorithm is verified via vehicle tests in real road. It has been shown that the threat assessment performance for the given driving situations can be significantly enhanced by the proposed algorithm. And this enhancement of risk assessment performance led to capabilities improvement of driver assistance functions of ADASs.

Robust Real-Time Optimal Autonomous Highway Driving Control System: Development and Implementation

Abstract This paper develops a systems approach for robust real-time optimal autonomous driving control system development. An autonomous vehicle control system framework is proposed to dissect an automotive system into some sub-systems in which physical systems and control softwares are communicating. To attain robustness, a robust on-road vehicle localization scheme is proposed applying multi sensor-data fusion with the results of multirate decentralized state estimation and the clothoidal road model constraint. Control block and control block topology are proposed to utilize block-wise perception of environment and vehicle localization and to produce a trajectory command via a virtual lane curve for longitudinal/lateral vehicle control. To attain real-time control optimality, we apply the multilevel approximate predictive control developed by the authors. Performance of the proposed autonomous driving control system is demonstrated through some track test results.