1,352 research outputs found
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
Pedestrian Prediction by Planning using Deep Neural Networks
Accurate traffic participant prediction is the prerequisite for collision
avoidance of autonomous vehicles. In this work, we predict pedestrians by
emulating their own motion planning. From online observations, we infer a
mixture density function for possible destinations. We use this result as the
goal states of a planning stage that performs motion prediction based on common
behavior patterns. The entire system is modeled as one monolithic neural
network and trained via inverse reinforcement learning. Experimental validation
on real world data shows the system's ability to predict both, destinations and
trajectories accurately
Real-time stereo semi-global matching for video processing using previous incremental information
This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios
LIMO: Lidar-Monocular Visual Odometry
Higher level functionality in autonomous driving depends strongly on a
precise motion estimate of the vehicle. Powerful algorithms have been
developed. However, their great majority focuses on either binocular imagery or
pure LIDAR measurements. The promising combination of camera and LIDAR for
visual localization has mostly been unattended. In this work we fill this gap,
by proposing a depth extraction algorithm from LIDAR measurements for camera
feature tracks and estimating motion by robustified keyframe based Bundle
Adjustment. Semantic labeling is used for outlier rejection and weighting of
vegetation landmarks. The capability of this sensor combination is demonstrated
on the competitive KITTI dataset, achieving a placement among the top 15. The
code is released to the community.Comment: Accepted at IROS 201
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