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A large number of sensors (i.e., video, radar, laser, ultrasound, etc.) that continuously monitor the environment are finding their way in the average automobile. The algorithms processing the data captured by these sensors are streaming in nature and require a high rate of computation. Due to the characteristics of the automotive environment, this computation has to be delivered under very low energy and cost budgets. The reconfigurable streaming vector processing (RSVP™) architecture is a vector coprocessor architecture which accelerates streaming data processing. This paper presents the RSVP architecture and its second implementation, RSVP II. Our results show significant speedups on data streaming functions running compiled code. On a lane tracking application, RSVP II shows impressive performance results. From a performance/$ and performance/mW perspective, RSVP architecture compares favorably with leading DSP architectures. The time to market is substantially reduced due to ease of programmability, elimination of hand-tuned assembly code, and support for software re-use through binary compatibility across multiple implementations.