A Faster and More Reliable Middleware for Autonomous Driving Systems
Ensuring safety in high-speed autonomous vehicles requires rapid control loops and tightly bounded delays from perception to actuation. Many open-source autonomy systems rely on ROS 2 middleware; when multiple sensor and control nodes share one compute unit, ROS 2 and its DDS transports add significant (de)serialization, copying, and discovery overheads, shrinking the available time budget. We present Sensor-in-Memory (SIM), a shared-memory transport designed for intra-host pipelines in autonomous vehicles. SIM keeps sensor data in native memory layouts (e.g., cv::Mat, PCL), uses lock-free bounded double buffers that overwrite old data to prioritize freshness, and integrates into ROS 2 nodes with four lines of code. Unlike traditional middleware, SIM operates beside ROS 2 and is optimized for applications where data freshness and minimal latency outweigh guaranteed completeness. SIM provides sequence numbers, a writer heartbeat, and optional checksums to ensure ordering, liveness, and basic integrity. On an NVIDIA Jetson Orin Nano, SIM reduces data-transport latency by up to 98% compared to ROS 2 zero-copy transports such as FastRTPS and Zenoh, lowers mean latency by about 95%, and narrows 95th/99th-percentile tail latencies by around 96%. In tests on a production-ready Level 4 vehicle running Autoware.Universe, SIM increased localization frequency from 7.5 Hz to 9.5 Hz. Applied across all latency-critical modules, SIM cut average perception-to-decision latency from 521.91 ms to 290.26 ms, reducing emergency braking distance at 40 mph (64 km/h) on dry concrete by 13.6 ft (4.14 m).
Code (0)
등록된 구현이 없습니다.
Tasks
Autonomous VehiclesAutonomous DrivingSimilar Papers 제목 키워드 기반
Service Discovery-Based Hybrid Network Middleware for Efficient Communication in Distributed Robotic Systems
Robotic middleware is fundamental to ensuring reliable communication among system components and is crucial for intelligent robotics, autonomous vehicles, and smart manufacturing. However, existing robotic middleware oft…
Autonomous VehiclesAutonomous DrivingHPRM: High-Performance Robotic Middleware for Intelligent Autonomous Systems
The rise of intelligent autonomous systems, especially in robotics and autonomous agents, has created a critical need for robust communication middleware that can ensure real-time processing of extensive sensor data. Cur…
Autonomous Drivingobject-detectionObject DetectionAn Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
Nowadays, autonomous driving has attracted much attention from both industry and academia. Convolutional neural network (CNN) is a key component in autonomous driving, which is also increasingly adopted in pervasive comp…
Autonomous DrivingRisk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
End-to-end paradigm has emerged as a promising approach to autonomous driving. However, existing single-agent end-to-end pipelines are often constrained by occlusion and limited perception range, resulting in hazardous d…
Trajectory PlanningAutonomous DrivingVAD: Vectorized Scene Representation for Efficient Autonomous Driving
Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representation (e.g., agent occupancy and semantic …
Autonomous DrivingBench2DriveTrajectory Planning