Open-Vocabulary Object Detection (OVOD) aims to detect both known and novel categories in complex visual scenes, surpassing the limitations of conventional closed-set detectors. Recent advances in vision-language models (VLMs) like CLIP have enabled zero-shot recognition by aligning visual features with large-scale textual embeddings. However, current OVOD approaches often fall short by overlooking critical contextual and semantic cues necessary for discovering a broader range of novel objects. To address this, we propose BFDet, a scene-to-object reasoning framework that leverages the complementary strengths of Large Language Models (LLMs) and VLMs. BFDet introduces a novel scene-to-object reasoning mechanism grounded in foreground-background context interaction. It first uses high-confidence objects to infer the scene-level background. This scene background then guides the discovery of foreground objects by prompting an LLM to generate scene-sensitive novel object candidates. These candidates are subsequently verified through cross-modal alignment and used as high-quality pseudo-labels to enrich detector training. Designed as a plug-and-play module, BFDet integrates seamlessly into existing detection pipelines and consistently improves performance on novel categories across COCO and LVIS benchmarks.

Real-time systems must simultaneously deliver predictable timing, fault isolation, and memory safety, yet current operating systems expose only low-level primitives that force developers to manually balance concurrency, isolation, and performance. This paper presents LARTS, a language-aided runtime system that elevates these requirements into language abstractions with enforceable semantics. LARTS introduces execution domain, a unified process–thread abstraction that combines thread-level responsiveness with process-level isolation. Memory is managed through deterministic memory contracts, which bind allocation at load time to eliminate runtime failures and unpredictable latencies. Domain interactions are expressed via deterministic communication channels that integrate efficient transfer, type safety, and priority inheritance, ensuring analyzable end-to-end bounds. Moreover, LARTS enforces secure-by-construction semantics, making classes of bugs such as double fetch and use-after-free unrepresentable in the programming model. We formalize the core semantics of LARTS and show how they guarantee determinism and safety by design. A prototype built on RTEMS demonstrates that LARTS preserves competitive real-time performance while substantially reducing programming complexity and eliminating vulnerabilities in realistic case studies. Our results suggest that high-assurance real-time programming can be treated not as an ad-hoc engineering problem, but as a first-class abstraction with verifiable semantics.

Lidar and optical camera are common sensors in the sensor layer of autopilot system. Lidar can use depth data to obtain accurate relative distance and contour information of obstacles, which is not easily affected by external light conditions. Optical camera can obtain rich object/environment semantic information through high-resolution image, which is relatively mature in technology. The two different sensors are highly complementary, and previous studies show that the fusion of laser point cloud and image data can greatly improve the efficiency of object detection in out door environment. In this paper, a deep convolutional neural network detection model based on Lidar and image information features layered fusion is studied. We try different fusion depth at the CNN model to seek the best solution according to the detection performance. The experimental results on the KITTI dataset show that the detection accuracy of the fusion based on YOLOv3 is 1.08% higher than original model. Another small scale experiment with our own self-driving platform on local area also show the final fusion model can achieve better detection accuracy in real road condition.