Challenges and Opportunities for Autonomous Vehicle Query Systems
Summary: AV query systems treat fleet-collected visual and spatial streams as a continuously-updating, partial digital twin enabling real-time queries (e.g., parking availability, queue lengths, road/sidewalk conditions). Unique research challenges: extreme multimodal volume, spatio-temporal bias, privacy/regulatory constraints, and new system-design trade-offs. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fiodar Kazhamiaka (Stanford University)
- 2. Matei Zaharia (Stanford University)
- 3. Peter Bailis (Stanford University)
BibTeX Citation
@inproceedings{kazhamiaka_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Challenges and Opportunities for Autonomous Vehicle Query Systems}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Kazhamiaka, Fiodar and Zaharia, Matei and Bailis, Peter},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,007 | Optimizing Video Analytics with Declarative Model Relationships | 2023 | VLDB | 6.9632395e-05 |
| 4,256 | VIVA: An End-to-End System for Interactive Video Analytics | 2022 | CIDR | 6.8018439e-05 |
| 10,573 | Incremental Stream Query Deployment under Continuous Infrastructure Changes in the Cloud-Edge Continuum | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 569 | BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics | 2020 | VLDB | 0.00016348191 |
| 3,875 | Visual Road: A Video Data Management Benchmark | 2019 | SIGMOD | 7.0557272e-05 |
| 4,991 | VisualWorldDB: A DBMS for the Visual World | 2020 | CIDR | 6.410552e-05 |
| 7,355 | Exploring big volume sensor data with Vroom | 2017 | VLDB | 5.6348348e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,670 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD |
| 2 | 12,822 | Integrated Data Management for Mobile Services in the Real World | 2003 | VLDB |
| 3 | 13,057 | What's Special about Spatial? Database Requirements for Vehicle Navigation in Geographic Space (Extended Abstract) | 1993 | SIGMOD |
| 4 | 10,125 | A Vision for Autonomous Data Agent Collaboration: From Query-by-Integration to Query-by-Collaboration | 2026 | CIDR |
| 5 | 10,393 | Query-Aware Path Inference from Spatial Videos | 2026 | SIGMOD |
| 6 | 1,149 | A Case for A Collaborative Query Management System | 2009 | CIDR |
| 7 | 11,499 | Towards Auto-Generated Data Systems | 2023 | VLDB |
| 8 | 12,008 | CarStream: An Industrial System of Big Data Processing for Internet-of-Vehicles | 2017 | VLDB |
| 9 | 4,991 | VisualWorldDB: A DBMS for the Visual World | 2020 | CIDR |
| 10 | 7,355 | Exploring big volume sensor data with Vroom | 2017 | VLDB |