In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance

Abstract

Data stream management systems (DSMSs) are suitable for managing and processing continuous data at high input rates with low latency. For advanced driver assistance including autonomous driving, embedded systems use a variety of onboard sensor data with communications from outside the vehicle. Thus, the software developed for such systems must be able to handle large volumes of data and complex processing. We develop a platform that integrates and manages data in an automotive embedded system using a DSMS. However, because automotive data processing, which is distributed in in-vehicle networks of the embedded system, is time-critical and must be reliable to reduce sensor noise, it is difficult to identify conventional DSMSs that meet these requirements. To address these new challenges, we develop an automotive embedded DSMS (AEDSMS). This AEDSMS precompiles high-level queries into executable query plans when designing automotive systems that demand time-criticality. Data stream processing is distributed in in-vehicle networks appropriately, where real-time scheduling and senor data fusion are also applied to meet deadlines and enhance the reliability of sensor data. The main contributions of this paper are as follows: (1) we establish a clear understanding of the challenges faced when introducing DSMSs into the automotive field; (2) we propose an AEDSMS to tackle these challenges; and (3) we evaluate the AEDSMS during run-time for advanced driver assistance.

Time-selective data fusion for in-network processing in ad hoc wireless sensor networks

“…The rest of the experiments (6)(7)(8)(9)(10)(11) in Table 3 show how averaged values for simultaneity interval and area of location estimation are influenced by alignment and selection algorithm together with different values for simultaneity constraints.…”

“…The data produced by periodic execution of sensor processes (as subscribed by fusion processes) form data streams which can be intermittent 4 with elements not uniformly distributed in time with the possibility of some elements being sporadically delayed due to behavioural pattern of ad hoc WSN. As a result, some of the stream data elements may violate the required validity periods, 3 arrive out of order 6,7 and can often have only partial temporal coverage. 8 This behaviour could be caused by several factors, such as the combined effect from the application of low data rate communication standard (e.g.…”