Database Paper Browser

Back to papers

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)

Paper ID
417
Venue
CIDR
Year
2021
Pagerank
4.5392079e-05
Overall Rank
8,286 | 42.42%
DOI
-

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Previous Page 1 / 1 Next

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
694 BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics 2020 VLDB 0.00018031141
4,078 Visual Road: A Video Data Management Benchmark 2019 SIGMOD 6.4633815e-05
5,038 VisualWorldDB: A DBMS for the Visual World 2020 CIDR 5.7376543e-05
6,721 Exploring big volume sensor data with Vroom 2017 VLDB 4.9457362e-05
Previous Page 1 / 1 Next

Semantically Similar Papers