Extended H<sub>∞</sub> Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation

Zubaca, Jasmina; Stolz, Michael; Watzenig, Daniel

doi:10.1109/cavs51000.2020.9334568

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Therefore, they have been widely adopted in lidarbased fusion localization systems. Zubača et al [35] proposed an extended H∞ filter with an adaptive new information sequence to fuse the measurement information of LiDAR, IMU, and other dynamic sensors of vehicles to improve the robustness and accuracy of vehicle post-estimation. Maaref et al [36] proposed a lane-level localization method that combines LiDAR odometry and cellular data pseudo-distance.…”

Section: Lidar-based Multi-sensor Fusion Localizationmentioning

confidence: 99%

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

Cao

Luo

et al. 2023

Remote Sensing

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The detection range and accuracy of light detection and ranging (LiDAR) systems are sensitive to variations in fog concentration, leading to the safety of the intended functionality-related (SOTIF-related) problems in the LiDAR-based fusion localization system (LMSFLS). However, due to the uncontrollable weather, it is almost impossible to quantitatively analyze the effects of fog on LMSFLS in a realistic environment. Therefore, in this study, we conduct a layered quantitative SOTIF analysis of the LMSFLS on foggy days using fog simulation. Based on the analysis results, we identify the component-level, system-level, and vehicle-level functional insufficiencies of the LMSFLS, the corresponding quantitative triggering conditions, and the potential SOTIF-related risks. To address the SOTIF-related risks, we propose a functional modification strategy that incorporates visibility recognition and a 3σ-criterion-based variance mismatch degree grading adaptive extended Kalman filter. The visibility of a scenario is recognized to judge whether the measurement information of the LiDAR odometry is disturbed by fog. Moreover, the proposed filter is adopted to fuse the abnormal measurement information of the LiDAR odometry with IMU and GNSS. Simulation results demonstrate that the proposed strategy can inhibit the divergence of the LMSFLS, improve the SOTIF of self-driving cars on foggy days, and accurately recognize the visibility of the scenarios.

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Section: Lidar-based Multi-sensor Fusion Localizationmentioning

confidence: 99%

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

Cao

Luo

et al. 2023

Remote Sensing

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“…This vehicle was equipped with sensors, actuation, and computation hardware to drive autonomously around the race track. Roborace gave interested student teams the opportunity to use this race car which displayed research in the field of localization (Massa et al, 2020;Zubaca et al, 2020), high dynamic path planning (Caporale et al, 2018;Stahl et al, 2019b) software development (Betz et al, 2019;Hermansdorfer et al, 2020) and control (Buyval et al, 2017;Wischnewski et al, 2019a). In addition, Roborace organized different competitions called Season Alpha and Season Beta that consisted of single-and multi-vehicle events on various race tracks.…”

Section: Hardwarementioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz¹,

Betz²,

Fent³

et al. 2022

Preprint

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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 −1 and 28 m s −2 . 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.

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“…This vehicle was equipped with sensors, actuation, and computation hardware to drive autonomously around the race track. Roborace gave interested student teams the opportunity to use this race car which displayed research in the field of localization (Massa et al, 2020; Zubaca et al, 2020), high dynamic path planning (Caporale et al, 2018; Stahl et al, 2019b), software development (Betz et al, 2019; Hermansdorfer et al, 2020), and control (Buyval et al, 2017; Wischnewski, Betz, et al, 2019). In addition, Roborace organized different competitions called Season Alpha and Season Beta that consisted of single‐ and multivehicle events on various race tracks.…”

Section: Introductionmentioning

confidence: 99%

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

Betz

Fent

et al. 2023

Journal of Field Robotics

View full text Add to dashboard Cite

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 IAC, 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 Technical University of Munich (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 2700.25em km h − 1 $270\,\text{km\hspace{0.05em}h}{}^{-1}$ and 280.25em m s − 2 $28\,ms{}^{-2}$. 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 2‐year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of 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.

show abstract

Extended H_∞ Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation

Cited by 5 publications

References 11 publications

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

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Extended H∞ Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation

Cited by 5 publications

References 11 publications

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

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Product

Resources

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Extended H_∞ Filter Adaptation Based on Innovation Sequence for Advanced Ego-Vehicle Motion Estimation