Record STMT-WD9E5A1Aspirational

Radar-Based Ego-Motion Estimation Using RadarScenes, Simulation, and Real-World Robot Data; Ego-Motion Estimation via Millimeter-Wave Radar SLAM Reconstructing Perception: Radar SLAM and the Ethics of Artificial Intelligence in Autonomous Navigation

Report Institutional policyNorthern Americaadopted

This undergraduate portfolio combines a technical capstone project on radar-based SLAM for ego-motion estimation in autonomous systems with an STS research paper analyzing the ethical and societal implications of machine perception. The technical component details the implementation of algorithms like the Kabsch algorithm and RANSAC to process noisy radar data for motion reconstruction. The STS component examines algorithmic governance, technological momentum, and the distribution of responsibility in safety-critical autonomous technologies. Together, the work argues that technical development and ethical analysis are inseparable, highlighting the need for transparency and cautious deployment.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

University of VirginiaAcademic

Publisher

Legal forceAspirational

Under whose authorityInstitutional policy

Judged fromfull text

Who it governs

Researchers and academicsGeneral publicAI developers and providersAI deployers and users

Researchers, developers, and students interested in the intersection of radar-based robot navigation and the ethical implications of machine perception.

Sectors

Transport and mobility · autonomous vehiclesResearch and science

What the document contains

What's covered

The document addresses the technical implementation of radar-based SLAM for ego-motion estimation using datasets, simulation, and real-world robot data, alongside an ethical analysis of machine perception, algorithmic governance, and accountability in autonomous systems.

What's missing

The document does not provide formal regulatory guidelines, legal enforcement mechanisms, industry standards, or specific policy definitions for AI governance.

Key takeaways

Radar-based SLAM pipelines can process raw sensor outputs into spatial representations for motion estimation, though they face challenges with noisy and sparse data.
Machine perception systems are frequently treated as objective and reliable despite being shaped by underlying design assumptions and limitations.
Algorithmic governance and technological momentum frameworks reveal how accountability becomes distributed and obscured across designers and institutions in autonomous systems.
Technical development and ethical analysis must be integrated to address the gap between perceived system reliability and actual performance in safety-critical contexts.
Responsible technology development requires greater transparency, clearer accountability structures, and more cautious deployment strategies.

Outline

· Abstract
· Technical Capstone Project: Radar-Based SLAM for Ego-Motion Estimation
· STS Research Project: Ethical and Societal Implications of Machine Perception
· Conclusion and Integration of Technical and Ethical Perspectives

Issued11 May 2026

Length871 words

FormatNot recorded

Scopeinstitutional

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

3/5

Clear responsibility for AI outcomes

The central question of my STS paper asks how we can assign responsibility and build trust in systems that rely on complex, often opaque forms of perception.

Abstract

screening score · 2026-10-23

Fairness

0/5

Non-discrimination and equitable treatment

screening score · 2026-10-23

Human Dignity

0/5

Respect for the inherent worth of persons

screening score · 2026-10-23

Human Oversight

0/5

Humans remain in control of consequential decisions

screening score · 2026-10-23

Privacy

0/5

Protection of personal data and private life

screening score · 2026-10-23

Transparency

3/5

Openness about how AI systems work and are used

My work suggests that greater transparency, clearer accountability structures, and more cautious deployment strategies are necessary to ensure that machine perception technologies are developed responsibly.

Abstract

screening score · 2026-10-23

Enforcement

0/5

Consequences and compliance mechanisms with real force

screening score · 2026-10-23

Impact Assessment

2/5

Required assessment of an AI system's risks/impacts before or during use

In parallel with this technical work, my STS research project examines the broader implications of machine perception systems, particularly in high-stakes contexts such as autonomous vehicles and AI driven decision-making.

Abstract

screening score · 2026-10-23

Topics

Physical AI Governancecontext

radar_slam algorithmic_governance autonomous_vehicles machine_perception technological_momentum