RACECAR -- The Dataset for High-Speed Autonomous Racing

This paper describes the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The dataset contains data from 27 racing sessions across the 11 scenarios with over 6.5 hours of sensor data recorded from the track. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing. We present several benchmark problems on localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping using the RACECAR data to explore issues that arise at the limits of operation of the vehicle.Comment: 9 pages, 10 figures. For links to data and reference material go to https://github.com/linklab-uva/RACECAR_DAT

Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware-enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course.Comment: 15 pages, 12 figures, 3 table

EDGAR: An Autonomous Driving Research Platform -- From Feature Development to Real-World Application

While current research and development of autonomous driving primarily focuses on developing new features and algorithms, the transfer from isolated software components into an entire software stack has been covered sparsely. Besides that, due to the complexity of autonomous software stacks and public road traffic, the optimal validation of entire stacks is an open research problem. Our paper targets these two aspects. We present our autonomous research vehicle EDGAR and its digital twin, a detailed virtual duplication of the vehicle. While the vehicle's setup is closely related to the state of the art, its virtual duplication is a valuable contribution as it is crucial for a consistent validation process from simulation to real-world tests. In addition, different development teams can work with the same model, making integration and testing of the software stacks much easier, significantly accelerating the development process. The real and virtual vehicles are embedded in a comprehensive development environment, which is also introduced. All parameters of the digital twin are provided open-source at https://github.com/TUMFTM/edgar_digital_twin

In Vivo Assessment of Neuroinflammation in 4-Repeat Tauopathies

Background: Neuroinflammation has received growing interest as a therapeutic target in neurodegenerative disorders, including 4-repeat tauopathies. Objectives: The aim of this cross-sectional study was to investigate 18 kDa translocator protein positron emission tomography (PET) as a biomarker for microglial activation in the 4-repeat tauopathies corticobasal degeneration and progressive supranuclear palsy. Methods Specific binding of the 18 kDa translocator protein tracer F-18-GE-180 was determined by serial PET during pharmacological depletion of microglia in a 4-repeat tau mouse model. The 18 kDa translocator protein PET was performed in 30 patients with corticobasal syndrome (68 +/- 9 years, 16 women) and 14 patients with progressive supranuclear palsy (69 +/- 9 years, 8 women), and 13 control subjects (70 +/- 7 years, 7 women). Group comparisons and associations with parameters of disease progression were assessed by region-based and voxel-wise analyses. Results Tracer binding was significantly reduced after pharmacological depletion of microglia in 4-repeat tau mice. Elevated 18 kDa translocator protein labeling was observed in the subcortical brain areas of patients with corticobasal syndrome and progressive supranuclear palsy when compared with controls and was most pronounced in the globus pallidus internus, whereas only patients with corticobasal syndrome showed additionally elevated tracer binding in motor and supplemental motor areas. The 18 kDa translocator protein labeling was not correlated with parameters of disease progression in corticobasal syndrome and progressive supranuclear palsy but allowed sensitive detection in patients with 4-repeat tauopathies by a multiregion classifier. Conclusions: Our data indicate that F-18-GE-180 PET detects microglial activation in the brain of patients with 4-repeat tauopathy, fitting to predilection sites of the phenotype. The 18 kDa translocator protein PET has a potential for monitoring neuroinflammation in 4-repeat tauopathies. (c) 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Societ

DeepSTEP -- Deep Learning-Based Spatio-Temporal End-To-End Perception for Autonomous Vehicles

Autonomous vehicles demand high accuracy and robustness of perception algorithms. To develop efficient and scalable perception algorithms, the maximum information should be extracted from the available sensor data. In this work, we present our concept for an end-to-end perception architecture, named DeepSTEP. The deep learning-based architecture processes raw sensor data from the camera, LiDAR, and RaDAR, and combines the extracted data in a deep fusion network. The output of this deep fusion network is a shared feature space, which is used by perception head networks to fulfill several perception tasks, such as object detection or local mapping. DeepSTEP incorporates multiple ideas to advance state of the art: First, combining detection and localization into a single pipeline allows for efficient processing to reduce computational overhead and further improves overall performance. Second, the architecture leverages the temporal domain by using a self-attention mechanism that focuses on the most important features. We believe that our concept of DeepSTEP will advance the development of end-to-end perception systems. The network will be deployed on our research vehicle, which will be used as a platform for data collection, real-world testing, and validation. In conclusion, DeepSTEP represents a significant advancement in the field of perception for autonomous vehicles. The architecture's end-to-end design, time-aware attention mechanism, and integration of multiple perception tasks make it a promising solution for real-world deployment. This research is a work in progress and presents the first concept of establishing a novel perception pipeline.Comment: Accepted to be published as part of the 5th Workshop on 3D-Deep Learning for Automated Driving on the 34th IEEE Intelligent Vehicles Symposium (IV), Anchorage, Alaska, USA, June 4-7, 202

Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems

The objective of this work is to provide a comprehensive understanding of the development of autonomous vehicle perception systems. So far, most autonomy perception research has been concentrated on improving perception systems’ algorithmic quality or combining different sensor setups. In our work, we draw conclusions from participating in the Indy Autonomous Challenge 2021 and its follow-up event in Las Vegas 2022. These were the first head-to-head autonomous racing competitions that required an entire perception pipeline to perceive the environment and the opposing surrounding vehicles. Our research includes quantitative results from collected vehicle data and qualitative results from simulation, video, and multiple race analysis. The Indy Autonomous Challenge was one of the few research projects that considered the entire autonomous vehicle. Therefore, our findings indicate insights on the system level, including hardware setup and full-stack software. We can demonstrate that different sensor modalities in the vehicle have strengths and weaknesses when they are deployed. Our results further show the difficulties and challenges that emerge when multi-modal perception systems must run in real-time on real-world autonomous vehicles. The most concise finding from our investigation is the summary of critical learnings when developing and deploying perception systems for autonomous systems. Given the background of the study, it was inevitable that our conclusions were influenced by driving on the racetrack and only one hardware setup available. Therefore, in the discussion, we draw further parallels to driving on public roads in dense traffic. More studies are needed to investigate the development and deployment of multi-modal perception systems for autonomous road vehicles with different hardware setups and various object detection, localization, and prediction algorithms. The novel contributions of this work are given by 12 lessons learned, summarized in 5 categories. These were derived and validated through a realized real-world application project. The videos of the final events in Indianapolis and Las Vegas can be watched here:IAC: https://www.youtube.com/watch?v=ERTffn3IpIs&ab_channel=CNETHighlightsAC@CES: https://www.youtube.com/watch?v=df9f4Qfa0uU&ab_channel=CNETHighlightsMultiple modules of the software stack are open source: https://github.com/TUMFTM

In Vivo Assessment of Neuroinflammation in 4-Repeat Tauopathies.

BACKGROUND Neuroinflammation has received growing interest as a therapeutic target in neurodegenerative disorders, including 4-repeat tauopathies. OBJECTIVES The aim of this cross-sectional study was to investigate 18 kDa translocator protein positron emission tomography (PET) as a biomarker for microglial activation in the 4-repeat tauopathies corticobasal degeneration and progressive supranuclear palsy. METHODS Specific binding of the 18 kDa translocator protein tracer 18 F-GE-180 was determined by serial PET during pharmacological depletion of microglia in a 4-repeat tau mouse model. The 18 kDa translocator protein PET was performed in 30 patients with corticobasal syndrome (68 ± 9 years, 16 women) and 14 patients with progressive supranuclear palsy (69 ± 9 years, 8 women), and 13 control subjects (70 ± 7 years, 7 women). Group comparisons and associations with parameters of disease progression were assessed by region-based and voxel-wise analyses. RESULTS Tracer binding was significantly reduced after pharmacological depletion of microglia in 4-repeat tau mice. Elevated 18 kDa translocator protein labeling was observed in the subcortical brain areas of patients with corticobasal syndrome and progressive supranuclear palsy when compared with controls and was most pronounced in the globus pallidus internus, whereas only patients with corticobasal syndrome showed additionally elevated tracer binding in motor and supplemental motor areas. The 18 kDa translocator protein labeling was not correlated with parameters of disease progression in corticobasal syndrome and progressive supranuclear palsy but allowed sensitive detection in patients with 4-repeat tauopathies by a multiregion classifier. CONCLUSIONS Our data indicate that 18 F-GE-180 PET detects microglial activation in the brain of patients with 4-repeat tauopathy, fitting to predilection sites of the phenotype. The 18 kDa translocator protein PET has a potential for monitoring neuroinflammation in 4-repeat tauopathies. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

TUM Autonomous Motorsport: An Autonomous Racing Software for the Indy Autonomous Challenge

For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems like disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the Indy Autonomous Challenge, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from TUM placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around 270 km/h and 28 ms2. This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the two-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. Based on this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts.Comment: 37 pages, 18 figures, 2 table

Asymmetry of Fibrillar Plaque Burden in Amyloid Mouse Models.

Asymmetries of amyloid-β (Aβ) burden are well known in Alzheimer disease (AD) but did not receive attention in Aβ mouse models of Alzheimer disease. Therefore, we investigated Aβ asymmetries in Aβ mouse models examined by Aβ small-animal PET and tested if such asymmetries have an association with microglial activation. Methods: We analyzed 523 cross-sectional Aβ PET scans of 5 different Aβ mouse models (APP/PS1, PS2APP, APP-SL70, App NL-G-F , and APPswe) together with 136 18-kDa translocator protein (TSPO) PET scans for microglial activation. The asymmetry index (AI) was calculated between tracer uptake in both hemispheres. AIs of Aβ PET were analyzed in correlation with TSPO PET AIs. Extrapolated required sample sizes were compared between analyses of single and combined hemispheres. Results: Relevant asymmetries of Aβ deposition were identified in at least 30% of all investigated mice. There was a significant correlation between AIs of Aβ PET and TSPO PET in 4 investigated Aβ mouse models (APP/PS1: R = 0.593, P = 0.001; PS2APP: R = 0.485, P = 0.019; APP-SL70: R = 0.410, P = 0.037; App NL-G-F : R = 0.385, P = 0.002). Asymmetry was associated with higher variance of tracer uptake in single hemispheres, leading to higher required sample sizes. Conclusion: Asymmetry of fibrillar plaque neuropathology occurs frequently in Aβ mouse models and acts as a potential confounder in experimental designs. Concomitant asymmetry of microglial activation indicates a neuroinflammatory component to hemispheric predominance of fibrillary amyloidosis. Keywords: amyloid; asymmetry; microglia; mouse models