A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation

Collin, Anne; Siddiqi, Afreen; Imanishi, Yuto; Matta, Yukti; Tanimichi, Taisetsu; Weck, Olivier de

doi:10.1007/978-3-030-34843-4_12

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…The recent literature on co-design of complex systems presents a framework for optimization with highly-coupled multidisciplinary constraints and objectives [17]. This approach has been applied to the Pareto-efficient design of autonomous drones [18] and self-driving vehicles [19], including sensor placement on an autonomous vehicle considering candidates formed by discretizing yaw angle [20]. Our method diverges from multidisciplinary design criteria and instead focuses on a much finer discretization of SE(3) to generate a comprehensive set of candidate sensor mountings.…”

Section: A Design Of Perception Systemsmentioning

confidence: 99%

Multi-camera sensing for robust perception in robotics

Kaveti¹

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The number and arrangement of sensors on an autonomous mobile robot dramatically influence its perception capabilities. Ensuring that sensors are mounted in a manner that enables accurate detection, localization, and mapping is essential for the success of downstream control tasks. However, when designing a new robotic platform, researchers and practitioners alike usually mimic standard configurations or maximize simple heuristics like field-of-view (FOV) coverage to decide where to place exteroceptive sensors. In this work, we conduct an information-theoretic investigation of this overlooked element of mobile robotic perception in the context of simultaneous localization and mapping (SLAM). We show how to formalize the sensor arrangement problem as a form of subset selection under the E-optimality performance criterion. While this formulation is NP-hard in general, we further show that a combination of greedy sensor selection and fast convex relaxation-based post-hoc verification enables the efficient recovery of certifiably optimal sensor designs in practice. Results from synthetic experiments reveal that sensors placed with OASIS outperform benchmarks in terms of mean squared error of visual SLAM estimates.

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Section: A Design Of Perception Systemsmentioning

confidence: 99%

Multi-camera sensing for robust perception in robotics

Kaveti¹

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OASIS: Optimal Arrangements for Sensing in SLAM

Kaveti,

Giamou,

Singh

et al. 2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Electrified Drives for Automated Vehicles

Sturm,

Küçükay,

Henze

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">This paper deals with the relationship between powertrain design and the requirements resulting from connected and automated driving. The questions addressed are how much powertrain design will change in regard to automated and connected driving and which powertrain in an automated vehicle will prove to be the optimum solution. To this end, a concept study is being conducted for a D-segment vehicle and multiple powertrain topologies ranging from non-electrified, mild-hybrids to plug-in hybrids and battery electric vehicles. The development processes required to address this issue is presented accordingly, as well as the necessary methods for systemic drive optimization, taking into account all requirements of the vehicle, the drive system and the components and their interactions with each other. The requirements resulting from connected and automated driving as well as their influences on vehicle and drive concepts are elaborated. The work focuses on automated driving functions developed at the Institute of Automotive Engineering of the Technische Universität Braunschweig, primarily on a “highway pilot”. The hardware architecture required to implement the automated driving functions leads to an increase in vehicle mass as well as additional power requirements. Automated driving also leads to changes in driving profiles during operation. On the basis of measurement data from the "highway pilot", representative driving cycles in terms of customer and function operation are created and used for drive optimization. The drives optimized for a vehicle with highway pilot lead to a significant reduction in fuel and energy consumption in customer operation. Electric vehicles are showing the highest savings potential (11% to 14%) followed by plug-in hybrids (5% to 14%). The consumption reduction potential also shows a strong dependence on the powertrain design and the powertrain topology. The combustion engine has a relatively large range with very high efficiencies, which is why the operating point distribution change due to automated driving only slightly influences the efficiency of the ICE, 34% without automated driving to 35% with automated driving. The results of the drive optimization are discussed in detail in the paper and the implications for future drive development are highlighted.</div></div>

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A Multiobjective Systems Architecture Model for Sensor Selection in Autonomous Vehicle Navigation

Cited by 3 publications

References 27 publications

Multi-camera sensing for robust perception in robotics

Multi-camera sensing for robust perception in robotics

OASIS: Optimal Arrangements for Sensing in SLAM

Electrified Drives for Automated Vehicles

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