Many safety-critical cyber-physical systems rely on advanced sensing capabilities to react to changing environmental conditions. One such domain is automotive systems. In this domain, a proliferation of advanced sensor technology is being fueled by an expanding range of autonomous capabilities (blind spot warnings, automatic lane-keeping, etc.). The limit of this expansion is full autonomy, which has been demonstrated in various one-off prototypes, but at the expensive of significant hardware over-provisioning that is not tenable for a consumer product. To enable features approaching full autonomy in a commercial vehicle, software infrastructure will be required that enables multiple sensor-processing streams to be multiplexed onto a common hardware platform at reasonable cost. This project is directed at the development of such infrastructure. The desired infrastructure will be developed by focusing on a particularly compelling challenge problem: enabling cost-effective driver-assist and autonomous-control automotive features that utilize vision-based sensing through cameras. This problem will be studied by (i) examining numerous multicore-based hardware configurations at various fixed price points based on realistic automotive use cases, and by (ii) characterizing the range of vision-based workloads that can be feasibly supported using the software infrastructure to be developed. The research to be conducted will be a collaboration involving academic researchers at UNC and engineers at General Motors Research. The collaborative nature of this effort increases the likelihood that the results obtained will have real impact in the U.S. automotive industry. Additionally, this project is expected to produce new open-source software and tools, new course content, public outreach through participation in UNC's demo program, and lectures and seminars by the investigators at national and international forums.