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

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

A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection

Object detection in camera images, using deep learning has been proven successfully in recent years. Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles. Nevertheless, the sensor quality of the camera is limited in severe weather conditions and through increased sensor noise in sparsely lit areas and at night. Our approach enhances current 2D object detection networks by fusing camera data and projected sparse radar data in the network layers. The proposed CameraRadarFusionNet (CRF-Net) automatically learns at which level the fusion of the sensor data is most beneficial for the detection result. Additionally, we introduce BlackIn, a training strategy inspired by Dropout, which focuses the learning on a specific sensor type. We show that the fusion network is able to outperform a state-of-the-art image-only network for two different datasets. The code for this research will be made available to the public at: https://github.com/TUMFTM/CameraRadarFusionNet.Comment: Accepted at 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF

Energy Management Strategy for an Autonomous Electric Racecar using Optimal Control

The automation of passenger vehicles is becoming more and more widespread, leading to full autonomy of cars within the next years. Furthermore, sustainable electric mobility is gaining in importance. As racecars have been a development platform for technology that has later also been transferred to passenger vehicles, a race format for autonomous electric racecars called Roborace has been created. As electric racecars only store a limited amount of energy, an Energy Management Strategy (EMS) is needed to work out the time as well as the minimum energy trajectories for the track. At the same time, the technical limitations and component behavior in the electric powertrain must be taken into account when calculating the race trajectories. In this paper, we present a concept for a special type of EMS. This is based on the Optimal Control Problem (OCP) of generating a time-minimal global trajectory which is solved by the transcription via direct orthogonal collocation to a Nonlinear Programming Problem (NLPP). We extend this minimum lap time problem by adding our ideas for a holistic EMS. This approach proves the fundamental feasibility of the stated ideas, e.g. varying racepaths and velocities due to energy limitations, covered by the EMS. Also, the presented concept forms the basis for future work on meta-models of the powertrain's components that can be fed into the OCP to increase the validity of the control output of the EMS.Comment: Accepted at the IEEE Intelligent Transportation Systems Conference - ITSC 2019, Auckland, New Zealand 27 - 30 Octobe

MixNet: Structured Deep Neural Motion Prediction for Autonomous Racing

Reliably predicting the motion of contestant vehicles surrounding an autonomous racecar is crucial for effective and performant planning. Although highly expressive, deep neural networks are black-box models, making their usage challenging in safety-critical applications, such as autonomous driving. In this paper, we introduce a structured way of forecasting the movement of opposing racecars with deep neural networks. The resulting set of possible output trajectories is constrained. Hence quality guarantees about the prediction can be given. We report the performance of the model by evaluating it together with an LSTM-based encoder-decoder architecture on data acquired from high-fidelity Hardware-in-the-Loop simulations. The proposed approach outperforms the baseline regarding the prediction accuracy but still fulfills the quality guarantees. Thus, a robust real-world application of the model is proven. The presented model was deployed on the racecar of the Technical University of Munich for the Indy Autonomous Challenge 2021. The code used in this research is available as open-source software at www.github.com/TUMFTM/MixNet

Case Study of Holistic Energy Management Using Genetic Algorithms in a Sliding Window Approach

Energy management systems are used to find a compromise between conflicting goals that can be identified for battery electric vehicles. Typically, these are the powertrain efficiency, the comfort of the driver, the driving dynamics, and the component aging. This paper introduces an optimization-based holistic energy management system for a battery electric vehicle. The energy management system can adapt the vehicle velocity and the power used for cabin heating, in order to minimize the overall energy consumption, while keeping the total driving time and the cabin temperature within predefined limits. A genetic algorithm is implemented in this paper. The approach is applied to different driving cycles, which are optimized by dividing them into distinctive time frames. This approach is referred to as the sliding window approach. The optimization is conducted with two separate driving cycles, the New European Driving Cycle (NEDC) and a recorded real-world drive. These are analyzed with regard to the aspects relevant to the energy management system, and the optimization results for the two cycles are compared. The results presented in this paper demonstrate the feasibility of the sliding window approach. Moreover, they reveal the differences in fundamental parameters between the NEDC and the recorded drive and how they affect the optimization results. The optimization leads to an overall reduction in energy consumption of "inline-formula" "math display="inline"" "semantics" "mrow" "mn"3.37"/mn" "mo"%"/mo" "/mrow" "/semantics" "/math" "/inline-formula" for the NEDC and "inline-formula" "math display="inline"" "semantics" "mrow" "mn"3.27"/mn" "mo"%"/mo" "/mrow" "/semantics" "/math" "/inline-formula" for the recorded drive, without extending the travel time. Document type: Articl