Sensitivity and Performance Evaluation of Multiple-Model State Estimation Algorithms for Autonomous Vehicle Functions

Abstract

Robust object tracking and maneuver estimation methods play significant role in the design of advanced driver assistant systems and self-driving cars. As an input to situation understanding and awareness, the performance of such algorithms influences the overall effectiveness of motion planning and plays high role in safety. The paper examines the suitability of different probabilistic state estimation methods, namely, the Extended Kalman Filter (EKF) and the more general Particle Filter (PF) with the addition of the Interacting Multiple Model (IMM) approach. These algorithms are not capable of predicting motion for long term in road traffic conditions, though their robustness and model classification capability are essential for the overall system. The performance is evaluated in road traffic scenarios where the tracked object imitates the motion characteristics of a road vehicle and is observed from a stationary sensor. The measurements are generated according to standard automotive radar models. The analysis conducted along two aspects emphasizes the different performance and scaling properties of the examined state estimation algorithms. The presented evaluation framework serves as a customizable method to test and develop advanced autonomous functions.

Control methods for the coordination of autonomous vehicles at intersections

“…Related to this field of control design there are several papers focusing on maneuvers and motion prediction of autonomous vehicles, see [15], [16], [17]. In connection with the driving intention of autonomous vehicles, other works approaches the detection problem of the maneuvers of vehicles considering the alert signals, like brake lights and turn signals, see [18], [19].…”

Vehicle State Estimation Based on Strong Tracking Central Different Kalman Filter

“…Vehicles equipped with active safety control systems can improve their handling stability and greatly reduce the incidence of traffic accidents significantly. How to obtain the driving state of the vehicle accurately and in real time has become the primary problem to be solved by the active safety control [1][2][3][4][5].…”

Innovative Interaction Approach in IMM Filtering for Vehicle Motion Models With Unequal States Dimension

“…[32]- [35]. It was applied in the autonomous driving field as well, to solve problems such as vehicle localization [36], multi-vehicle tracking with road maps [37], and evaluation of autonomous driving functions [38]. Furthermore, a motion planning framework for urban autonomous driving at uncontrolled intersections was developed in [39].…”