206 research outputs found
Phase transition for loop representations of Quantum spin systems on trees
We consider a model of random loops on Galton-Watson trees with an offspring
distribution with high expectation. We give the configurations a weighting of
. For many these models are equivalent to
certain quantum spin systems for various choices of the system parameters. We
find conditions on the offspring distribution that guarantee the occurrence of
a phase transition from finite to infinite loops for the Galton-Watson tree.Comment: 16 pages, 1 figur
Benchmarking of a software stack for autonomous racing against a professional human race driver
The way to full autonomy of public road vehicles requires the step-by-step
replacement of the human driver, with the ultimate goal of replacing the driver
completely. Eventually, the driving software has to be able to handle all
situations that occur on its own, even emergency situations. These particular
situations require extreme combined braking and steering actions at the limits
of handling to avoid an accident or to diminish its consequences. An average
human driver is not trained to handle such extreme and rarely occurring
situations and therefore often fails to do so. However, professional race
drivers are trained to drive a vehicle utilizing the maximum amount of possible
tire forces. These abilities are of high interest for the development of
autonomous driving software. Here, we compare a professional race driver and
our software stack developed for autonomous racing with data analysis
techniques established in motorsports. The goal of this research is to derive
indications for further improvement of the performance of our software and to
identify areas where it still fails to meet the performance level of the human
race driver. Our results are used to extend our software's capabilities and
also to incorporate our findings into the research and development of public
road autonomous vehicles.Comment: Accepted at 2020 Fifteenth International Conference on Ecological
Vehicles and Renewable Energies (EVER
Therapeutic Prospects of Metabolically Active Brown Adipose Tissue in Humans
The world-wide obesity epidemic constitutes a severe threat to human health and wellbeing and poses a major challenge to health-care systems. Current therapeutic approaches, relying mainly on reduced energy intake and/or increased exercise energy expenditure, are generally of limited effectiveness. Previously believed to be present only in children, the existence of metabolically active brown adipose tissue (BAT) was recently demonstrated also in healthy human adults. The physiological role of BAT is to dissipate chemical energy, mainly from fatty acids, as heat to maintain body temperature in cold environments. Recent studies indicate that the activity of BAT is negatively correlated with overweight and obesity, findings that raise the exciting possibility of new and effective weight reduction therapies based on increased BAT energy expenditure, a process likely to be amenable to pharmacological intervention
Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios
Trajectory planning at high velocities and at the handling limits is a
challenging task. In order to cope with the requirements of a race scenario, we
propose a far-sighted two step, multi-layered graph-based trajectory planner,
capable to run with speeds up to 212~km/h. The planner is designed to generate
an action set of multiple drivable trajectories, allowing an adjacent behavior
planner to pick the most appropriate action for the global state in the scene.
This method serves objectives such as race line tracking, following, stopping,
overtaking and a velocity profile which enables a handling of the vehicle at
the limit of friction. Thereby, it provides a high update rate, a far planning
horizon and solutions to non-convex scenarios. The capabilities of the proposed
method are demonstrated in simulation and on a real race vehicle.Comment: Accepted at The 22nd IEEE International Conference on Intelligent
Transportation Systems, October 27 - 30, 201
Minimum Race-Time Planning-Strategy for an Autonomous Electric Racecar
Increasing attention to autonomous passenger vehicles has also attracted
interest in an autonomous racing series. Because of this, platforms such as
Roborace and the Indy Autonomous Challenge are currently evolving. Electric
racecars face the challenge of a limited amount of stored energy within their
batteries. Furthermore, the thermodynamical influence of an all-electric
powertrain on the race performance is crucial. Severe damage can occur to the
powertrain components when thermally overstressed. In this work we present a
race-time minimal control strategy deduced from an Optimal Control Problem
(OCP) that is transcribed into a Nonlinear Problem (NLP). Its optimization
variables stem from the driving dynamics as well as from a thermodynamical
description of the electric powertrain. We deduce the necessary first-order
Ordinary Differential Equations (ODE)s and form simplified loss models for the
implementation within the numerical optimization. The significant influence of
the powertrain behavior on the race strategy is shown.Comment: Accepted at The 23rd IEEE International Conference on Intelligent
Transportation Systems, September 20 - 23, 202
Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles
Current validation methods often rely on recorded data and basic functional
checks, which may not be sufficient to encompass the scenarios an autonomous
vehicle might encounter. In addition, there is a growing need for complex
scenarios with changing vehicle interactions for comprehensive validation. This
work introduces a novel synchronous multi-agent simulation framework for
autonomous vehicles in interactive scenarios. Our approach creates an
interactive scenario and incorporates publicly available edge-case scenarios
wherein simulated vehicles are replaced by agents navigating to predefined
destinations. We provide a platform that enables the integration of different
autonomous driving planning methodologies and includes a set of evaluation
metrics to assess autonomous driving behavior. Our study explores different
planning setups and adjusts simulation complexity to test the framework's
adaptability and performance. Results highlight the critical role of simulating
vehicle interactions to enhance autonomous driving systems. Our setup offers
unique insights for developing advanced algorithms for complex driving tasks to
accelerate future investigations and developments in this field. The
multi-agent simulation framework is available as open-source software:
https://github.com/TUM-AVS/Frenetix-Motion-PlannerComment: 8 Pages. Submitted to IEEE IV 2024 Korea Conferenc
A Stochastic Nonlinear Model Predictive Control with an Uncertainty Propagation Horizon for Autonomous Vehicle Motion Control
Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time
applications is challenging due to the complex task of propagating
uncertainties through nonlinear systems. This difficulty becomes more
pronounced in high-dimensional systems with extended prediction horizons, such
as autonomous vehicles. To enhance closed-loop performance in and feasibility
in SNMPCs, we introduce the concept of the Uncertainty Propagation Horizon
(UPH). The UPH limits the time for uncertainty propagation through system
dynamics, preventing trajectory divergence, optimizing feedback loop
advantages, and reducing computational overhead. Our SNMPC approach utilizes
Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates
nonlinear hard constraints on state expectations and nonlinear probabilistic
constraints. We transform the probabilistic constraints into deterministic
constraints by estimating the nonlinear constraints' expectation and variance.
We then showcase our algorithm's effectiveness in real-time control of a
high-dimensional, highly nonlinear system-the trajectory following of an
autonomous passenger vehicle, modeled with a dynamic nonlinear single-track
model. Experimental results demonstrate our approach's robust capability to
follow an optimal racetrack trajectory at speeds of up to 37.5m/s while dealing
with state estimation disturbances, achieving a minimum solving frequency of
97Hz. Additionally, our experiments illustrate that limiting the UPH renders
previously infeasible SNMPC problems feasible, even when incorrect uncertainty
assumptions or strong disturbances are present
RNMPC: A Real-Time Reduced Robustified Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets for Autonomous Vehicle Motion Control
In this paper, we present a novel Reduced Robustified NMPC (RNMPC)
algorithm that has the same complexity as an equivalent nominal NMPC while
enhancing it with robustified constraints based on the dynamics of ellipsoidal
uncertainty sets. This promises both a closed-loop- and constraint satisfaction
performance equivalent to common Robustified NMPC approaches, while drastically
reducing the computational complexity. The main idea lies in approximating the
ellipsoidal uncertainty sets propagation over the prediction horizon with the
system dynamics' sensitivities inferred from the last optimal control problem
(OCP) solution, and similarly for the gradients to robustify the constraints.
Thus, we do not require the decision variables related to the uncertainty
propagation within the OCP, rendering it computationally tractable. Next, we
illustrate the real-time control capabilities of our algorithm in handling a
complex, high-dimensional, and highly nonlinear system, namely the trajectory
following of an autonomous passenger vehicle modeled with a dynamic nonlinear
single-track model. Our experimental findings, alongside a comparative
assessment against other Robust NMPC approaches, affirm the robustness of our
method in effectively tracking an optimal racetrack trajectory while satisfying
the nonlinear constraints. This performance is achieved while fully utilizing
the vehicle's interface limits, even at high speeds of up to 37.5m/s, and
successfully managing state estimation disturbances. Remarkably, our approach
maintains a mean solving frequency of 144Hz
A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control
Determining the optimal cost function parameters of Model Predictive Control
(MPC) to optimize multiple control objectives is a challenging and
time-consuming task. Multiobjective Bayesian Optimization (BO) techniques solve
this problem by determining a Pareto optimal parameter set for an MPC with
static weights. However, a single parameter set may not deliver the most
optimal closed-loop control performance when the context of the MPC operating
conditions changes during its operation, urging the need to adapt the cost
function weights at runtime. Deep Reinforcement Learning (RL) algorithms can
automatically learn context-dependent optimal parameter sets and dynamically
adapt for a Weightsvarying MPC (WMPC). However, learning cost function weights
from scratch in a continuous action space may lead to unsafe operating states.
To solve this, we propose a novel approach limiting the RL actions within a
safe learning space representing a catalog of pre-optimized BO Pareto-optimal
weight sets. We conceive a RL agent not to learn in a continuous space but to
proactively anticipate upcoming control tasks and to choose the most optimal
discrete actions, each corresponding to a single set of Pareto optimal weights,
context-dependent. Hence, even an untrained RL agent guarantees a safe and
optimal performance. Experimental results demonstrate that an untrained RL-WMPC
shows Pareto-optimal closed-loop behavior and training the RL-WMPC helps
exhibit a performance beyond the Pareto-front
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