Context-model generation for safe autonomous transport vehicles

2013 IEEE 14th International Conference on Mobile Data Management

Bolles

Funk

et al. 2013

Self Cite

Due to the fact that currently operating autonomous vehicles can observe only a limited area with their onboard sensors, safety regulations often dictate a very slow speed. However, as more and more sensors in the environment are available, we can fuse their information and provide extended information as a shared context model to support the autonomous vehicles. In this paper, we consider a scenario with a publicly accessible area that is populated with autonomous transport vehicles, human guided vehicles like trucks or bicycles, and pedestrians. We analyze requirements and challenges for highly dynamic context models in this scenario. Furthermore, we propose a comprehensive system architecture that can cope with these challenges, namely deterministic processing of multiple sensor updates with high throughput rates, prediction of moving objects, and on-line quality assessments, and demonstrate the feasibility of this approach by implementing the generic system architecture with laser scanners for object detection.

Section: Methodsmentioning

confidence: 99%

SaLsA Streams: Dynamic Context Models for Autonomous Transport Vehicles Based on Multi-sensor Fusion

2013 IEEE 14th International Conference on Mobile Data Management

Bolles

Funk

et al. 2013

Self Cite

“…The Odysseus system was already successfully used for the DEBS'12 Grand Challenge [2]. Further, it has been shown, that the system is capable to process high frequent data in different domains, e.g., autonomous transport vehicles [10], which was also demonstrated at the DEBS'12 [11]. The DSMS uses a stream-based variant of the relational algebra, which is a well-known and common concept in relational data base systems to provide semantically correct and deterministic solutions.…”

Section: Demonstrationmentioning

confidence: 98%

Supporting quality-aware pervasive applications by probabilistic data stream management

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

Nicklas

2014

Self Cite

Many pervasive computing applications need sensor data streams, which can vary significantly in accuracy. Depending on the application, deriving information (e.g., higherlevel context) from low-quality sensor data might lead to wrong decisions or even critical situations. Thus, it is important to control the quality throughout the whole data stream processing, from the raw sensor data up to the derived information, e.g., a complex event. In this paper, we describe the demonstration of the integration of a uniform metadata model to represent sensor data and information quality at all levels of processing in a data stream processing engine to ease the development of quality-aware applications.

“…The Odysseus DSMS was already successfully used for context model generation using occupancy grids based on laser scanner data for autonomous vehicle [22,21]. The DSMS uses the interval approach for processing sensor data in a push-based manner using the relational operators.…”

Section: Odysseusmentioning

confidence: 99%

Quality matters

Proceedings of the 8th ACM International Conference on Distributed Event-Based Systems

Nicklas

2014

Self Cite

Many pervasive computing applications need sensor data streams, which can vary significantly in accuracy. Depending on the application, deriving information (e.g., higherlevel context) from low-quality sensor data might lead to wrong decisions or even critical situations. Thus, it is important to control the quality throughout the whole data stream processing, from the raw sensor data up to the derived information, e.g., a complex event. In this paper, we present a uniform meta data model to represent sensor data and information quality at all levels of processing; we show how this meta data model can be integrated in a data stream processing engine to ease the development of quality-aware applications; and we present an approach to learn probability distributions of incoming sensor data which needs no prior knowledge. We demonstrate and evaluate our approach in a real-world scenario.