EFFECT OF SENSOR ERRORS ON AUTONOMOUS STEERING CONTROL AND APPLICATION OF SENSOR FUSION FOR ROBUST NAVIGATION

Autonomous steering control is one the most important features in autonomous vehicle navigation. The nature and tuning of the controller decides how well the vehicle follows a defined trajectory. A poorly tuned controller can cause the vehicle to oversteer or understeer at turns leading to deviation from a defined path. However, controller performance also depends on the state–feedback system. If the states used for controller input are noisy or has bias / systematic error, the navigation performance of the vehicle is affected irrespective of the control law and controller tuning. In this report, autonomous steering controller analysis is done for different kinds of sensor errors and the application of sensor fusion using Kalman Filters is discussed. Model-in-the-loop (MIL) simulation provides an efficient way for developing and performing controller analysis and implementing various fusion algorithms. Matlab/Simulink was used for this Model Based Development. Firstly, through experimentation the path tracking performance of the controller was analyzed followed by data collection for sensor, actuator and vehicle modelling. Then, the plant, actuator and controllers were modelled followed by the comparison of the results for ideal and non-ideal sensors. After analyzing the effects of sensor error on controller and vehicle performance, a solution was proposed using 1D-Kalman Filter (KF) based sensor fusion technique. It is seen that the waypoint tracking under 1D condition is improved to centimeter level and the steering response is also smoothened due to less noisy vehicle heading estimation

On ramp metering: Towards a better understanding of ALINEA via model-free control

International audienceALINEA, which was introduced almost thirty years ago, remains certainly the most well known feedback loop for ramp metering control. A theoretical proof of its efficiency at least when the traffic conditions are rather mild is given here, perhaps for the first time. It relies on tools stemming from the new model-free control and the corresponding "intelligent" proportional controllers. Several computer experiments confirm our theoretical investigations

Real-Time Vehicle Classification System Using a Single Magnetometer

Vehicle count and classification data are very important inputs for intelligent transportation systems (ITS). Magnetic sensor-based technology provides a very promising solution for the measurement of different traffic parameters. In this work, a novel, real-time vehicle detection and classification system is presented using a single magnetometer. The detection, feature extraction, and classification are performed online, so there is no need for external equipment to conduct the necessary computation. Data acquisition was performed in a real environment using a unit installed into the surface of the pavement. A very large number of samples were collected containing measurements of various vehicle classes, which were applied for the training and the validation of the proposed algorithm. To explore the capabilities of magnetometers, nine defined vehicle classes were applied, which is much higher than in relevant methods. The classification is performed using three-layer feedforward artificial neural networks (ANN). Only time-domain analysis was performed on the waveforms using multiple novel feature extraction approaches. The applied time-domain features require low computation and memory resources, which enables easier implementation and real-time operation. Various combinations of used sensor axes were also examined to reduce the size of the classifier and to increase efficiency. The effect of the detection length, which is a widely used feature, but also speed-dependent, on the proposed system was also investigated to explore the suitability of the applied feature set. The results show that the highest achieved classification efficiencies on unknown samples are 74.67% with, and 73.73% without applying the detection length in the feature set