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Safely and accurately navigating needles percutaneously or endoscopically to sites deep within the body is essential for many medical procedures, from biopsies to localized drug deliveries to tumor ablations. The advent of image guidance decades ago gave physicians information about the patient’s anatomy. We are now entering the era of AI (artificial intelligence) guidance, where AI can automatically analyze images, identify targets and obstacles, compute safe trajectories, and autonomously navigate a needle to a site with unprecedented accuracy and precision. We survey recent advances in the building blocks of AI guidance for medical needle deployment robots (perceiving anatomy, planning motions, perceiving instrument state, and performing motions) and discuss research opportunities to maximize the benefits of AI guidance for patient care. 

This paper presents a real-time locking protocol whose design was motivated by the goal of enabling safe GPU sharing in time-sliced component-based systems. This locking protocol enables a GPU to be shared concurrently across, and utilized within, isolated components with predictable execution times. It relies on a novel resizing technique where GPU work is dimensioned on-the-fly to run on partitions of an NVIDIA GPU. This technique can be applied to any component that internally utilizes global CPU scheduling. The proposed locking protocol enables increased GPU parallelism and reduces GPU capacity loss with analytically provable benefits. 

The goal of a real-time locking protocol is to reduce any priority-inversion blocking (pi-blocking) a task may incur while waiting to access a shared resource. For mutual-exclusion sharing on an m-processor platform, the best existing lower bound on per-task pi-blocking under suspension-oblivious analysis is a (trivial) lower bound of (m-1) request lengths under any job-level fixed-priority (JLFP) scheduler. Surprisingly, most asymptotically optimal locking protocols achieve a per-task pi-blocking upper bound of (2m-1) request lengths under JLFP scheduling, even though a range of very different mechanisms are used in these protocols. This paper closes the gap between these existing lower and upper bounds by establishing a lower bound of (2m-2) request lengths under global fixed-priority (G-FP) and global earliest-deadline-first (G-EDF) scheduling. This paper also shows that worst-case per-task pi-blocking can be arbitrarily close to (2m-1) request lengths for locking protocols that satisfy a certain property that is met by most (if not all) existing locking protocols. These results imply that most known asymptotically optimal locking protocols are almost truly optimal (not just asymptotic) under G-FP and G-EDF scheduling. 

Machine-learning (ML) technology has been a key enabler in the push towards realizing ever more sophisticated autonomous-driving features. In deploying such technology, the automotive industry has relied heavily on using "black-box" software and hardware components that were originally intended for non-safety-critical contexts, without a full understanding of their real-time capabilities. A prime example of such a component is CUDA, which is fundamental to the acceleration of ML algorithms using NVIDIA GPUs. In this paper, evidence is presented demonstrating that CUDA can cause unbounded task delays. Such delays are the result of CUDA’s usage of synchronization mechanisms in the POSIX thread (pthread) library, so the latter is implicated as a delay-prone component as well. Such synchronization delays are shown to be the source of a system failure that occurred in an actual autonomous vehicle system during testing at WeRide. Motivated by these findings, a broader experimental study is presented that demonstrates several real-time deficiencies in CUDA, the glibc pthread library, Linux, and the POSIX interface of the safety-certified QNX Operating System for Safety. Partial mitigations for these deficiencies are presented and further actions are proposed for real-time researchers and developers to integrate more complete mitigations. 

Due to the emergence of parallel architectures and parallel programming frameworks, modern real-time applications are often composed of parallel tasks that can occupy multiple processors at the same time. Among parallel task models, gang scheduling has received much attention in recent years due to its performance efficiency and applicability to parallel architectures such as graphics processing units. Despite this attention, the soft real-time (SRT) scheduling of gang tasks has received little attention. This paper, for the first time, considers the SRT-feasibility problem for gang tasks. Necessary and sufficient feasibility conditions are presented that relate the SRTfeasibility problem to the HRT-feasibility problem of “equivalent” task systems. Based on these conditions, intractability results for SRT gang scheduling are derived. This paper also presents server-based scheduling policies, corresponding schedulability tests, and an improved schedulability condition for the global-earlies-tdeadline-first (GEDF) scheduling of gang tasks. Moreover, GEDF is shown to be non-optimal in scheduling SRT gang tasks. 

To certify the schedulability of a system, valid per-task worst-case execution-time (WCET) estimates are almost always required. Unfortunately, on multicore machines, deriving WCET estimates through static analysis that is not highly pessimistic may never be a practical reality. The alternative is to determine WCETs via a measurement process, but such a process cannot correctly produce accurate WCET estimates with certainty. This lack of certainty necessitates the use of overrun-handling mechanisms, such as budget-enforcement techniques, to preserve temporal correctness at runtime. In many systems of interest today, tasks are interconnected to form processing graphs, which can be quite large. The simplest (and perhaps most common) approach to budget enforcement in this case is to abort an entire graph invocation whenever any node (task) overruns its budget. However, such an approach can result in a high abort rate at the graph level even when the per-node abort rate is low. To remedy this situation, this paper presents a holistic budget-management strategy for directed acyclic graphs (DAGs) that involves reallocating per-node budgets to overrunning nodes to avoid DAG-level aborts. To enable the effects of aborts to be studied analytically, a probabilistic analysis is presented to derive a DAG’s abort rate under the proposed budget-management strategy. Experimental results are also presented to demonstrate the utility of budgeting graphs holistically 

A medical robot can autonomously steer a needle to targets in vivo.