Johannes Betz

Johannes Betz
University of Pennsylvania | UP · Department of Electrical & Systems

Dr.-Ing.

About

80
Publications
35,881
Reads
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635
Citations
Introduction
Johannes Betz currently works at the University of Pennsylvania (Penn) as a Postdoctoral Researcher. He is member of the mLab: Real-Time and Embedded Systems Lab. His research is about cyber-physical-systems with focus on autonomous driving and Ai based design space exploration. If you want to learn about my research please visit www.joebetz.science
Additional affiliations
October 2020 - present
University of Pennsylvania
Position
  • PostDoc Position
Description
  • School of Engineering and Applied Science Department of Electrical & Systems Engineering mLAB: Real-Time and Embedded Systems Lab
November 2018 - September 2020
Technische Universität München
Position
  • PostDoc Position
Description
  • Institute of Automotive Technology Teamleader research group "Vehicle dynamcis and control systems" Topics: Autonomous driving, vehicle dynamcis, simulation, control and optimization methods, sensor fusion, embedded automotive systems, active safety systems, real-time modeling and signal processing Project manager of the TUM-Roborace project
November 2013 - November 2018
Technische Universität München
Position
  • PhD Student
Description
  • Research group "Smart Mobility" Projects: - Development of the EE architecture of the Visio.M vehicle - Development of a vehicle fleet disposition model for battery electric vehicles - Vehicle data acquisition, postprocessing and analysis
Education
October 2016 - April 2020
Technische Universität München
Field of study
  • Philosophy
November 2013 - November 2018
Technische Universität München
Field of study
  • Automotive Technology
March 2012 - November 2013
University of Bayreuth
Field of study
  • Automotive Technology

Publications

Publications (80)
Chapter
Full-text available
Whether BMW, VW or Google: Almost all leading automobile and technology companies are researching and developing the multi-stage autonomy of vehicles, which enables a completely self-driven vehicle without a driver in autonomy level 5. Based on his assessment and experience, the driver had previously carried out environmental detection, localizatio...
Article
This paper shows a software stack capable of planning a minimum curvature trajectory for an autonomous race car on the basis of an occupancy grid map and introduces a controller design that allows to follow the trajectory at the handling limits. The minimum curvature path is generated using a quadratic optimisation problem (QP) formulation. The key...
Conference Paper
Full-text available
This paper presents a detailed description of the software architecture that is used in the autonomous Roborace vehicles by the TUM-Team. The development of the software architecture was driven by both hardware components and usage of open source languages for making the software architecture reusable and easy to understand. The architecture combin...
Conference Paper
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 increas...
Preprint
Full-text available
The rising popularity of self-driving cars has led to the emergence of a new research field in the recent years: Autonomous racing. Researchers are developing software and hardware for high performance race vehicles which aim to operate autonomously on the edge of the vehicles limits: High speeds, high accelerations, low reaction times, highly unce...
Preprint
Full-text available
The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach inclu...
Preprint
Full-text available
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 f...
Chapter
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Autonomous Motorsports will participate in the Indy Autonomous Challenge in October 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapoli...
Article
Scenario understanding and motion prediction are essential components for completely replacing human drivers and for enabling highly and fully automated driving (SAE-Level 4/5). In deeply stochastic and uncertain traffic scenarios, autonomous driving software must act beyond existing traffic rules and must predict critical situations in advance to...
Preprint
Full-text available
Autonomous systems are composed of several subsystems such as mechanical, propulsion, perception, planning and control. These are traditionally designed separately which makes performance optimization of the integrated system a significant challenge. In this paper, we study the problem of using gradient-free optimization methods to jointly optimize...
Preprint
Full-text available
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapo...
Preprint
Full-text available
In literature, scientists describe human mobility in a range of granularities by several different models. Using frameworks like MATSIM, VehiLux, or Sumo, they often derive individual human movement indicators in their most detail. However, such agent-based models tend to be difficult and require much information and computational power to correctl...
Article
Solving a Minimum Lap Time Problem (MLTP), under the constraints stemming from a race car's driving dynamics, can be considered to be state of the art. Nevertheless, when dealing with electric race vehicles as in Formula E or the Roborace competition, solving an MLTP is not enough to form an appropriate competition strategy: Maximum performance ove...
Article
Full-text available
Autonomous Racing is gaining increasing publicity as an attractive showcase of state-of-the-art technologies and the enhanced algorithms used for autonomous driving. The Indy Autonomous Challenge (IAC) tackled autonomous high-speed wheel-to-wheel racing at the famous Indianapolis Motor Speedway (IMS) in October 2021. To solve this problem, advanced...
Article
Full-text available
The rising popularity of self-driving cars has led to the emergence of a new research field inrecent years: Autonomous racing. Researchers are developing software and hardware for high-performancerace vehicles which aim to operate autonomously on the edge of the vehicle’s limits: High speeds, highaccelerations, low reaction times, highly uncertain,...
Article
Full-text available
In 2017, the German ethics commission for automated and connected driving released 20 ethical guidelines for autonomous vehicles. It is now up to the research and industrial sectors to enhance the development of autonomous vehicles based on such guidelines. In the current state of the art, we find studies on how ethical theories can be integrated....
Preprint
Full-text available
High-performance autonomy often must operate at the boundaries of safety. When external agents are present in a system, the process of ensuring safety without sacrificing performance becomes extremely difficult. In this paper, we present an approach to stress test such systems based on the rapidly exploring random tree (RRT) algorithm. We propose t...
Preprint
Full-text available
The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial p...
Preprint
Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and weather conditions that need to be handled. In contrast to more constrained environments, such as automated underground trains, automotive perception systems cannot be tailored to a narrow field of specific tasks but must handle an ever-changing environment...
Article
Full-text available
Automotive traffic scenes are complex due to the variety of possible scenarios, objects, and weather conditions that need to be handled. In contrast to more constrained environments, such as automated underground trains, automotive perception systems cannot be tailored to a narrow field of specific tasks but must handle an ever-changing environment...
Article
Full-text available
State-of-the-art 3D object detection for autonomous driving is achieved by processing lidar sensor data with deep-learning methods. However, the detection quality of the state of the art is still far from enabling safe driving in all conditions. Additional sensor modalities need to be used to increase the confidence and robustness of the overall de...
Article
Full-text available
Connected and autonomous vehicles (CAVs) could reduce emissions, increase road safety, and enhance ride comfort. Multiple CAVs can form a CAV platoon with a close inter-vehicle distance, which can further improve energy efficiency, save space, and reduce travel time. To date, there have been few detailed studies of self-driving algorithms for CAV p...
Preprint
With the evolution of self-driving cars, autonomous racing series like Roborace and the Indy Autonomous Challenge are rapidly attracting growing attention. Researchers participating in these competitions hope to subsequently transfer their developed functionality to passenger vehicles, in order to improve self-driving technology for reasons of safe...
Conference Paper
Performance and robustness targets have been considered for controller design for decades. However, robust controllers usually suffer from performance limitations due to conservative uncertainty assumptions made a priori to system operation. The increased number of systems (e.g. autonomous vehicles) which require high-performance operation in safet...
Book
The market for commercial vehicles offers great potential for the use of electric vehicles today and in the future due to its spatial mobility behaviour. With a disposition model, which is combined with an energy management system and a charging management system, it should be possible to carry out optimal deployment planning for conventional and e...
Article
Full-text available
In circuit motorsport, race strategy helps to finish the race in the best possible position by optimally determining the pit stops. Depending on the racing series, pit stops are needed to replace worn-out tires, refuel the car, change drivers, or repair the car. Assuming a race without opponents and considering only tire degradation, the optimal ra...
Conference Paper
n this paper we give an overview on methods for the optical detection of road surface damages and analyze the importance of contextual information. The objective is to improve the optical detection of road damages, especially potholes, based on images from windscreen mounted monocular cameras, as well as to reduce the complexity and thus save compu...
Conference Paper
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...
Conference Paper
Electric vhicles and autonomous driving dominate current research efforts in the automotive sector. The two topics go hand in hand in terms of enabling safer and more environmentally friendly driving. One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map. In this p...
Code
Our dynamic, graph-based trajectory planner for race vehicles is now available open-source on GitHub! The planner demonstrated competitive behavior on a real race vehicle during the Roborace Season Alpha at speeds over 200kph, as well as in the simulative competition of the Indy Autonomous Challenge Hackaton 2. The planner is available on GitHub:...
Conference Paper
The development of software components for autonomous driving functions should al-ways include an extensive and rigorous evaluation. Since real-world testing is expensive and safety-critical – especially when facing dynamic racing scenarios at the limit of handling – a favored approach is simulation-based testing. In this work, we pro-pose an open-...
Code
The Scenario Architect provides a lightweight graphical user interface that allows a straightforward realization and manipulation of concrete driving testing scenarios. Exemplary use-cases are the validation of an online verification framework or training of a prediction algorithm. The Scenario Architect is available on GitHub: https://github.com/...
Article
Full-text available
Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race resu...
Preprint
Full-text available
The development of software components for autonomous driving functions should always include an extensive and rigorous evaluation. Since real-world testing is expensive and safety-critical -- especially when facing dynamic racing scenarios at the limit of handling -- a favored approach is simulation-based testing. In this work, we propose an open-...
Preprint
Full-text available
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...
Preprint
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 traject...
Preprint
Full-text available
3D object detection based on monocular camera data is a key enabler for autonomous driving. The task however, is ill-posed due to lack of depth information in 2D images. Recent deep learning methods show promising results to recover depth information from single images by learning priors about the environment. Several competing strategies tackle th...
Preprint
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 e...
Preprint
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 increas...
Preprint
Full-text available
Electric vhicles and autonomous driving dominate current research efforts in the automotive sector. The two topics go hand in hand in terms of enabling safer and more environmentally friendly driving. One fundamental building block of an autonomous vehicle is the ability to build a map of the environment and localize itself on such a map. In this p...
Preprint
Full-text available
Regulatory approval and safety guarantees for autonomous vehicles facing frequent functional updates and complex software stacks, including artificial intelligence, are a challenging topic. This paper proposes a concept and guideline for the development of an online verification module -- the Supervisor -- capable of handling the aforementioned cha...
Preprint
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 inf...
Article
Full-text available
Since 2017, a research team from the Technical University of Munich has developed a software stack for autonomous driving. The software was used to participate in the Roborace Season Alpha Championship. The championship aims to achieve autonomous race cars competing with different software stacks against each other. In May 2019, during a software t...
Conference Paper
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 traject...
Article
Full-text available
Typically, lane departure warning systems rely on lane lines being present on the road.However, in many scenarios, e.g., secondary roads or some streets in cities, lane lines are eithernot present or not sufficiently well signaled. In this work, we present a vision-based method tolocate a vehicle within the road when no lane lines are present using...
Conference Paper
Autonomous driving requires accurate information about the vehicle pose and motion state in order to achieve precise tracking of the planned trajectory. In this paper we propose a robust architecture to localize a high performance race car and show experimental results with speeds up to 150 km h-1 and utilizing approximately 80% of the available fr...
Conference Paper
In motorsports, lap time simulation (LTS) is used by race engineers to evaluate the effects of setup changes on lap time and energy consumption. Many of the LTS published to date are no longer able to meet today’s requirements because more and more racing series are introducing hybrid systems to improve powertrain efficiency. In addition, some raci...