Self-driving cars typically rely on a set of radars for mapping the environment to avoid obstacles and operate safely. Currently, the radar parameters are uncoordinated and can interfere with each other. This results in inefficient use of the spectrum and, more importantly, in dangerous blinding of the radars especially in dense urban settings, posing a barrier to widespread deployment of self-driving cars. We present RadarMAC - the first system architecture and dynamic radar parameter assignment algorithms for radar interference mitigation in self-driving cars. We characterize the degrees of freedom for vehicular radar parameters, and model the channel parameter assignment problem as one of dynamically coloring the corresponding time-varying interference graph. RadarMAC guarantees interference-free radar operations within a defined capacity region. Using extensive simulations, we demonstrate that RadarMAC significantly outperforms the state-of-the-art random assignment scheme commonly used today with off-the-shelf radars in terms of safety and spectrum efficiency metrics in both dense and sparse settings. Finally, we identify a set of research challenges in medium access control for vehicular radars.

Recent advances in parallel computing have made it possible for scientists to perform atomistic simulations of materials involving billions of atoms. An immersive and interactive virtual environment such as ImmersaDesk is an ideal platform for exploring complex material processes in these simulations. However rendering such large datasets at an interactive speed is a major challenge. To solve this problem we have developed a visualization system by incorporating parallel and distributed computing paradigms. The system uses a parallelized fast visibility-culling algorithm based on the octree data structure to reduce the number of atoms sent to the graphics pipeline. An adaptive multiresolution algorithm based on atomic density is employed to further reduce the load on the graphics pipeline. The resulting system renders a billion-atom system at nearly interactive frame rates on a dual processor SGI Onyx2 with an InfiniteReality2 graphics pipeline connected to a 4-node PC cluster.

A suite of scalable atomistic simulation programs has been developed for materials research based on space-time multiresolution algorithms. Design and analysis of parallel algorithms are presented for molecular dynamics (MD) simulations and quantum-mechanical (QM) calculations based on the density functional theory. Performance tests have been carried out on 1,088-processor Cray T3E and 1,280-processor IBM SP3 computers. The linear-scaling algorithms have enabled 6.44-billion-atom MD and 111,000-atom QM calculations on 1,024 SP3 processors with parallel efficiency well over 90%. The production-quality programs also feature wavelet-based computational-space decomposition for adaptive load balancing, spacefilling-curve-based adaptive data compression with user-defined error bound for scalable I/O, and octree-based fast visibility culling for immersive and interactive visualization of massive simulation data.