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

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

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

3D Printing of Polymer Hydrogels : From Basic Techniques to Programmable Actuation

This review discusses the currently available 3D printing approaches, design concepts, and materials that are used to obtain programmable hydrogel actuators. These polymer materials can undergo complex, predetermined types of motion and thereby imitate adaptive natural actuators with anisotropic, hierarchical substructures. 3D printing techniques allow replicating these complex shapes with immense design flexibility. While 3D printing of thermoplastic polymers has become a mainstream technique in rapid prototyping, additive manufacturing of softer polymers including polymer hydrogels is still challenging. To avoid deliquescence of printed hydrogel structures, the polymer inks used for hydrogel manufacture need to be sheer-thinning and thixotropic, with fast recovery rates of the high viscosity state. This is achieved by adding polymer or particle-based viscosity modifiers. Further stabilization of the interfaces of the printed voxels, e.g., by UV cross-linking, is often also required to obtain materials with useful mechanical properties. Here state-of-the-art techniques used to 3D print stimulus responsive, programmable polymer hydrogels, and hydrogel actuators, as well as ink formulation and post-printing strategies used to obtain materials with structural integrity are reviewed

“Just Antimicrobial is not Enough” Revisited : From Antimicrobial Polymers to Microstructured Dual‐Functional Surfaces, Self‐Regenerating Polymer Surfaces, and Polymer Materials with Switchable Bioactivity

Biofilm formation can be slowed down by restricting protein adhesion on a surface, or by antimicrobial/biocidal activity of the material (among other methods). In this progress report, the recent work on alternatives to single component antimicrobial or protein-repellent polymer materials is presented. These are microstructured bifunctional polymer surfaces and self-regenerating polymer multilayer stacks. The microstructured polymer surfaces consist of antimicrobial, protein-adhesive polymer patches, and nonfouling, protein repellent-polymer patches. By carefully balancing the size and architecture of the adhesive and repellent patches, materials with simultaneous antimicrobial activity and strong protein repellency are obtained. At similar polymer patch sizes, protein adhesion is lower on hydrogels with a low elastic modulus than on polymer monolayers attached to stiff substrates. Surface-regenerating polymer multilayer stacks are constructed from alternating layers of antimicrobial polymer hydrogels and degradable, soluble, or depolymerizable sacrificial layers. Top layer shedding, which imitates reptiles shedding their skin, rejuvenates the surface, and regenerates the antimicrobial function of the material. Layer shedding form such materials in solution is a competition between two thermodynamic minima, top layer reattachment and top layer removal. The outcome of each shedding event depends on the kinetics of the sacrificial layer disintegration

Self‐Regenerating of Functional Polymer Surfaces by Triggered Layer Shedding Using a Stimulus‐Responsive Poly(urethane)

Regeneration of functional surfaces after damage or contamination could extend the life time of devices. Such regeneration can be achieved by layer shedding (like a lizard shedding its skin). In this work, triggered self-regeneration of functional surfaces by an external stimulus is presented. Polymer multilayer stacks are assembled alternatingly from discrete 20–300 nm thick functional layers and depolymerizable interlayers, which are used as sacrificial layers. The sacrificial layers are depolymerizable poly(benzyl carbamates) end-capped with 4-hydroxy-2-butanone. Their depolymerization is triggered by alkaline pH, at which the end-cap is cleaved. This initiates a 1,6-elimination cascade of the polymer backbone, during which CO2 is released. Thus, the layer shedding is driven synergistically by mass transport and buoyancy forces. Proof-of-concept is achieved using poly(styrene) as a model functional layer, and also studied for hydrophilic, antimicrobially active poly(oxanorbornene) layers. The multilayer assembly and disassembly process is monitored by ellipsometry, Fourier transform infrared spectroscopy (FTIR), optical microscopy, and atomic force microscopy. FTIR spectra taken after degradation are confirmed the regeneration of the surface functionality

RadarGNN: Transformation Invariant Graph Neural Network for Radar-based Perception

A reliable perception has to be robust against challenging environmental conditions. Therefore, recent efforts focused on the use of radar sensors in addition to camera and lidar sensors for perception applications. However, the sparsity of radar point clouds and the poor data availability remain challenging for current perception methods. To address these challenges, a novel graph neural network is proposed that does not just use the information of the points themselves but also the relationships between the points. The model is designed to consider both point features and point-pair features, embedded in the edges of the graph. Furthermore, a general approach for achieving transformation invariance is proposed which is robust against unseen scenarios and also counteracts the limited data availability. The transformation invariance is achieved by an invariant data representation rather than an invariant model architecture, making it applicable to other methods. The proposed RadarGNN model outperforms all previous methods on the RadarScenes dataset. In addition, the effects of different invariances on the object detection and semantic segmentation quality are investigated. The code is made available as open-source software under https://github.com/TUMFTM/RadarGNN.Comment: Accepted to CVPR 2023 Workshop on Autonomous Driving (WAD

Requirements for Electric Machine Design based on Operating Points from Real Driving Data in Cities

Increasing environmental awareness leads to the necessity for more efficient powertrains in the future. However, the development of new vehicle concepts generates a trend towards ever shorter development cycles. Therefore, new concepts must be tested and validated at an early stage in order to meet the increasing time pressure. This requires the determination of real driving data in fleet tests in order to generate realistic driving cycles, which correspond as closely as possible to the actual driving behavior of the applications use case. Within the scope of this paper, real driving data are analyzed and used to create a representative driving cycle. The resulting driving cycle based on real driving characteristics is then used to investigate the impact of application-based design for powertrains on the design of electric machines, by illustrating the difference between synthetic operating points and real driving data. Document type: Articl