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Scalable Multi-modal model predictive control via Duality-based Interaction Predictions

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arXiv 2024年

作者： Kim, Hansung Nair, Siddharth H. Borrelli, Francesco The Model Predictive Control Laboratory UC Berkeley United States

We propose a hierarchical architecture designed for scalable real-time model predictive control (MPC) in complex, multi-modal traffic scenarios. This architecture comprises two key components: 1) RAID-Net, a novel attention-based Recurrent Neural Network that predicts relevant interactions along the MPC prediction horizon between the autonomous vehicle and the surrounding vehicles using Lagrangian duality, and 2) a reduced Stochastic MPC problem that eliminates irrelevant collision avoidance constraints, enhancing computational efficiency. Our approach is demonstrated in a simulated traffic intersection with interactive surrounding vehicles, showcasing a 12x speed-up in solving the motion planning problem. A video demonstrating the proposed architecture in multiple complex traffic scenarios can be found here: link. GitHub: link. Copyright © 2024, The Authors. All rights reserved.

关键词： model predictive control

predictive control for Autonomous Driving with Uncertain, Multi-modal Predictions

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arXiv 2023年

作者： Nair, Siddharth H. Lee, Hotae Joa, Eunhyek Wang, Yan Tseng, H. Eric Borrelli, Francesco The Model Predictive Control Laboratory UC Berkeley United States Ford Research and Advanced Engineering

We propose a Stochastic MPC (SMPC) formulation for path planning with autonomous vehicles in scenarios involving multiple agents with multi-modal predictions. The multi-modal predictions capture the uncertainty of urban driving in distinct modes/maneuvers (e.g., yield, keep speed) and driving trajectories (e.g., speed, turning radius), which are incorporated for multi-modal collision avoidance chance constraints for path planning. In the presence of multi-modal uncertainties, it is challenging to reliably compute feasible path planning solutions at real-time frequencies (≥ 10Hz). Our main technological contribution is a convex SMPC formulation that simultaneously (1) optimizes over parameterized feedback policies and (2) allocates risk levels for each mode of the prediction. The use of feedback policies and risk allocation enhances the feasibility and performance of the SMPC formulation against multi-modal predictions with large uncertainty. We evaluate our approach via simulations and road experiments with a full-scale vehicle interacting in closed-loop with virtual vehicles. We consider distinct, multi-modal driving scenarios: 1) Negotiating a traffic light and a fast, tailgating agent, 2) Executing an unprotected left turn at a traffic intersection, and 3) Changing lanes in the presence of multiple agents. For all of these scenarios, our approach reliably computes multi-modal solutions to the path-planning problem at real-time frequencies. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using model predictive control

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Collision Avoidance for Dynamic Obstacles with Uncertain Pre...

IEEE Conference on Decision and control

作者： Siddharth H. Nair Eric H. Tseng Francesco Borrelli Model Predictive Control Laboratory UC Berkeley Ford Research and Advanced Engineering

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

We propose a model predictive control (MPC) for collision avoidance between an autonomous agent and dynamic obstacles with uncertain predictions. The collision avoidance constraints are imposed by enforcing positive distance between convex sets representing the agent and the obstacles, and tractably reformulating them using Lagrange duality. This approach allows for smooth collision avoidance constraints even for polytopes, which otherwise require mixed-integer or non-smooth constraints. We consider three widely used descriptions of the uncertain obstacle position: 1) Arbitrary distribution with polytopic support, 2) Gaussian distributions and 3) Arbitrary distribution with first two moments known, and obtain deterministic reformulations of the collision avoidance constraints. The proposed MPC formulation optimizes over feedback policies to reduce conservatism in satisfying the collision avoidance constraints. The proposed approach is validated using simulations of traffic intersections in CARLA.

关键词： Geometry Uncertainty Gaussian distribution Autonomous agents Numerical models Collision avoidance Lagrangian functions

Interaction and Decision Making-aware Motion Planning using Branch model predictive control

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arXiv 2023年

作者： Oliveira, Rui Nair, Siddharth H. Wahlberg, Bo Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Model Predictive Control Laboratory UC Berkeley Berkeley United States

Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively making decisions based on the current state of the traffic scene. We propose a motion planning framework based on Branch model predictive control to deal with these challenges. The multi-modality is addressed by considering multiple future outcomes associated with different decisions taken by the human driver. The interactive nature of humans is considered by modeling them as reactive agents impacted by the actions of the autonomous vehicle. Finally, we consider a model developed in human neuroscience studies as a possible way of encoding the decision making process of human drivers. We present simulation results in various scenarios, showing the advantages of the proposed method and its ability to plan assertive maneuvers that convey intent to humans. © 2023, CC BY.

关键词： Motion planning

Collision Avoidance for Dynamic Obstacles with Uncertain Predictions using model predictive control

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arXiv 2022年

作者： Nair, Siddharth H. Tseng, Eric H. Borrelli, Francesco Model Predictive Control Laboratory UC Berkeley United States Ford Research and Advanced Engineering

We propose a model predictive control (MPC) for collision avoidance between an autonomous agent and dynamic obstacles with uncertain predictions. The collision avoidance constraints are imposed by enforcing positive distance between convex sets representing the agent and the obstacles, and tractably reformulating them using Lagrange duality. This approach allows for smooth collision avoidance constraints even for polytopes, which otherwise require mixed-integer or non-smooth constraints. We consider three widely used descriptions of the uncertain obstacle position: 1) Arbitrary distribution with polytopic support, 2) Gaussian distributions and 3) Arbitrary distribution with first two moments known. For each case we obtain deterministic reformulations of the collision avoidance constraints. The proposed MPC formulation optimizes over feedback policies to reduce conservatism in satisfying the collision avoidance constraints. The proposed approach is validated using simulations of traffic intersections in CARLA. Copyright © 2022, The Authors. All rights reserved.

关键词： Collision avoidance

Interaction and Decision Making-aware Motion Planning using Branch model predictive control

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Interaction and Decision Making-aware Motion Planning using ...

IEEE Symposium on Intelligent Vehicle

作者： Rui Oliveira Siddharth H. Nair Bo Wahlberg Scania Autonomous Transport Solutions Södertälje Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Model Predictive Control Laboratory UC Berkeley Berkeley USA

关键词：

Monocular Camera Localization for Automated Vehicles Using Image Retrieval

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arXiv 2021年

作者： Joa, Eunhyek Sun, Yibo Borrelli, Francesco The Model Predictive Control Laboratory University of California Berkeley United States

We address the problem of finding the current position and heading angle of an autonomous vehicle in real-time using a single camera. Compared to methods which require LiDARs and high definition (HD) 3D maps in real-time, the proposed approach is easily scalable and computationally efficient, at the price of lower precision. The new method combines and adapts existing algorithms in three different fields: image retrieval, mapping database, and particle filtering. The result is a simple, real-time localization method using an image retrieval method whose performance is comparable to other monocular camera localization methods which use a map built with LiDARs. We evaluate the proposed method using the KITTI odometry dataset and via closed-loop experiments with an indoor 1:10 autonomous vehicle. The tests demonstrate real-time capability and a 10cm level accuracy. Also, experimental results of the closed-loop indoor tests show the presence of a positive feedback loop between the localization error and the control error. Such phenomena is analysed in details at the end of the article. Copyright © 2021, The Authors. All rights reserved.

关键词： Image retrieval

Stochastic MPC with Multi-modal Predictions for Traffic Intersections

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arXiv 2021年

作者： Nair, Siddharth H. Govindarajan, Vijay Lin, Theresa Meissen, Chris Tseng, H. Eric Borrelli, Francesco The Model Predictive Control Laboratory UC Berkeley United States Ford AV LLC Ford Research and Advanced Engineering United States

We propose a Stochastic MPC (SMPC) formulation for autonomous driving at traffic intersections which incorporates multi-modal predictions of surrounding vehicles given by Gaussian Mixture models (GMM) for collision avoidance constraints. Our main theoretical contribution is a SMPC formulation that optimizes over a novel feedback policy class designed to exploit additional structure in the GMM predictions, and that is amenable to convex programming. The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios. We evaluate our algorithm along axes of mobility, comfort, conservatism and computational efficiency at a simulated intersection in CARLA. Our simulations use a kinematic bicycle model and multimodal predictions trained on a subset of the Lyft Level 5 prediction dataset. To demonstrate the impact of optimizing over feedback policies, we compare our algorithm with two SMPC baselines that handle multi-modal collision avoidance chance constraints by optimizing over openloop sequences. Copyright © 2021, The Authors. All rights reserved.

关键词： Stochastic systems

Robust control of a Lightweight Structure for Digital Fabrication

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IFAC-PapersOnLine 2020年第2期53卷 7424-7430页

作者： Yvonne R. Stürz Andrea Iannelli Roy S. Smith Model Predictive Control Laboratory University of California Berkeley CA 94709 USA Automatic Control Laboratory ETH Zürich ZH 8092 Switzerland

For a material efficient construction process of lightweight concrete shells, tensioned cable nets can be used as a supporting formwork. In order to guarantee the mechanical stability of the shells, tight tolerances in their form need to be met. To this end, methods have recently been proposed to readjust the form of the cable net on the construction site. This paper proposes a novel view on the cable net model as a dynamical system and derives robust control approaches based on H ∞ and μ synthesis. Both approaches can account for input uncertainties and external disturbances. The μ controller additionally is robust against the uncertain model parameters, which makes their precise identification unnecessary. The μ synthesis also provides a priori bounds on the allowable fabrication tolerances of the cable net in order to guarantee robust performance of the closed-loop system. The resulting LTI controllers can be applied to the system without expert knowledge about the system model or the underlying optimization. The effectiveness of the controllers is demonstrated in numerical experiments.

关键词： Robust control model Uncertainty μ -Synthesis Digital Fabrication Lightweight Structures