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.
Highlights
Nowadays, highly automated driver assistance systems and autonomous vehicles are in focus of attention and pose many different challenges that need to be solved
Classic object tracking algorithms are not suitable for mid-term motion prediction because they cannot consider the interaction of participants, though their robustness is essential for the proper input generation for these algorithms
Using nonideal Interacting Multiple Model (IMM), our study examines the effects of the hyperparameter tuning of the IMM, in a road vehicle-like environment, where the model for the sensor imitates the capabilities and performance of radar sensing
Summary
Highly automated driver assistance systems and autonomous vehicles are in focus of attention and pose many different challenges that need to be solved. The local and short term decisions require the autonomous vehicle to have some ability to reason about the future motion of surrounding vehicles [3] This leads to the problem of behavior prediction, where the ego vehicle needs to predict the possible future trajectories of the surrounding traffic participants, such as vehicles or pedestrians [4]. While the study in [18] states that the performance differences are highly dependent on the maneuvers of the target This solution assumed a so-called perfect IMM (PIMM), which is a lower bound error estimation, since it never fails to choose the appropriate model.
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