A critical task for developing safe autonomous driving stacks is to determine whether an obstacle is safety-critical, i.e., poses an imminent threat to the autonomous vehicle. Our previous work showed that Hamilton Jacobi reachability theory can be applied to compute interaction-dynamics-aware perception safety zones that better inform an ego vehicle’s perception module which obstacles are considered safety-critical. For completeness, these zones are typically larger than absolutely necessary, forcing the perception module to pay attention to a larger collection of objects for the sake of conservatism. As an improvement, we propose a maneuver-based decomposition of our safety zones that leverages information about the ego maneuver to reduce the zone volume. In particular, we propose a "temporal convolution" operation that produces safety zones for specific ego maneuvers, thus limiting the ego’s behavior to reduce the size of the safety zones. We show with numerical experiments that maneuver-based zones are significantly smaller (up to 76% size reduction) than the baseline while maintaining completeness.

To enable safe autonomous vehicle (AV) operations, it is critical that an AV’s obstacle detection module can reliably detect obstacles that pose a safety threat (i.e., are safety-critical). It is therefore desirable that the evaluation metric for the perception system captures the safety-criticality of objects. Unfortunately, existing perception evaluation metrics tend to make strong assumptions about the objects and ignore the dynamic interactions between agents, and thus do not accurately capture the safety risks in reality. To address these shortcomings, we introduce an interaction-dynamics-aware obstacle detection evaluation metric by accounting for closed-loop dynamic interactions between an ego vehicle and obstacles in the scene. By borrowing existing theory from optimal control theory, namely Hamilton-Jacobi reachability, we present a computationally tractable method for constructing a “safety zone a region in state space that defines where safety-critical obstacles lie for the purpose of defining safety metrics. Our proposed safety zone is mathematically complete, and can be easily computed to reflect a variety of safety requirements. Using an off-the-shelf detection algorithm from the nuScenes detection challenge leaderboard, we demonstrate that our approach is computationally lightweight, and can better capture safety-critical perception errors than a baseline approach.

Traditional graphics hardware architectures implement what we call the push architecture for texture mapping. Local memory is dedicated to the accelerator for fast local retrieval of texture during rasterization, and the application is responsible for managing this memory. The push architecture has a bandwidth advantage, but disadvantages of limited texture capacity, escalation of accelerator memory requirements (and therefore cost), and poor memory utilization. The push architecture also requires the programmer to solve the bin-packing problem of managing accelerator memory each frame. More recently graphics hardware on PC-class machines has moved to an implementation of what we call the pull architecture. Texture is stored in system memory and downloaded by the accelerator as needed. The pull architecture has advantages of texture capacity, stems the escalation of accelerator memory requirements, and has good memory utilization. It also frees the programmer from accelerator texture memory management. However, the pull architecture suffers escalating requirements for bandwidth from main memory to the accelerator. In this paper we propose multi-level texture caching to provide the accelerator with the bandwidth advantages of the push architecture combined with the capacity advantages of the pull architecture. We have studied the feasibility of 2-level caching and found the following: (1) significant re-use of texture between frames; (2) L2 caching requires significantly less memory than the push architecture; (3) L2 caching requires significantly less bandwidth from host memory than the pull architecture; (4) L2 caching enables implementation of smaller L1 caches that would otherwise bandwidth-limit accelerators on the workloads in this paper. Results suggest that an L2 cache achieves the original advantage of the pull architecture stemming the growth of local texture memory - while at the same time stemming the current explosion in demand for texture bandwidth between host memory and the accelerator.

In the area of scientific visualization, input data sets are often very large. In visualization of computational fluid dynamics (CFD) in particular, input data sets today can surpass 100 Gbytes, and are expected to scale with the ability of supercomputers to generate them. Some visualization tools already partition large data sets into segments, and load appropriate segments as they are needed. However, this does not remove the problem for two reasons: 1) there are data sets for which even the individual segments are too large for the largest graphics workstations, 2) many practitioners do not have access to workstations with the memory capacity required to load even a segment, especially since the state-of-the-art visualization tools tend to be developed by researchers with much more powerful machines. When the size of the data that must be accessed is larger than the size of memory, some form of virtual memory is simply required. This may be by segmentation, paging, or by paged segments. The authors demonstrate that complete reliance on operating system virtual memory for out-of-core visualization leads to egregious performance. They then describe a paged segment system that they have implemented, and explore the principles of memory management that can be employed by the application for out-of-core visualization. They show that application control over some of these can significantly improve performance. They show that sparse traversal can be exploited by loading only those data actually required.