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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
4.5435639e-05
Overall Rank
8,293 | 42.31%
DOI
-

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
696 BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics 2020 VLDB 0.00018048935
4,075 Visual Road: A Video Data Management Benchmark 2019 SIGMOD 6.4691764e-05
5,039 VisualWorldDB: A DBMS for the Visual World 2020 CIDR 5.7425824e-05
6,720 Exploring big volume sensor data with Vroom 2017 VLDB 4.9504873e-05
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