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Autonomous Driving System with Real-Time-Paper

Papers about real-time researches for autonomous driving systems

Target venues:

  • Real-time System Conferences (RTSS/RTAS/ECRTS/RTCSA);
  • Computer Architecture Conferences (ISCA/MICRO/HPCA/ASPLOS);
  • Operating System Conferences (OSDI/SOSP);
  • Computing System Conferences (MobiSys/EuroSys/SEC/ICDCS/ICCAD/ICCD/DAC/DATE)
  • Computer Network Conferences (SIGCOMM/NSDI/Mobicom/Infocom);
  • So many others (CVPR/ML4AD/ICRA).

Real-Time Analysis

[RTCSA'23] Safety-Aware Implementation of Control Tasks via Scheduling with Period Boosting and Compressing
[RTAS'23] Compositional Mixed-Criticality Systems with Multiple Executions and Resource-Budgets Model
[RTAS'23] Real-Time Scheduling of Autonomous Driving System with Guaranteed Timing Correctness
[RTAS'23] Minimizing Probabilistic End-to-end Latencies of Autonomous Driving Systems
[RTAS'20] Timing of Autonomous Driving Software: Problem Analysis and Prospects for Future Solutions

DNNs in ADS

[RTAS'19] Re-Thinking CNN Frameworks for Time-Sensitive Autonomous-Driving Applications: Addressing an Industrial Challenge
[RTSS'22] Prophet: Realizing a Predictable Real-time Perception Pipeline for Autonomous Vehicles
[Invited-Paper] CPT: A Configurable Predictability Testbed for DNN Inference in AVs
[ArXiv'22] Understanding Time Variations of DNN Inference in Autonomous Driving
[ML4AD'23] Data-parallel Real-Time Perception System with Partial GPU Acceleration for Autonomous Driving
[RTAS'23] Work In Progress: A New Task Model for Real-Time DNNs over GPU
[RTSS'20] On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines
[RTAS'22] Self-Cueing Real-Time Attention Scheduling in Criticality-Aware Visual Machine Perception
[RTAS'22] DNN-SAM: Split-and-Merge DNN Execution for Real-Time Object Detection
[ICCAD'21] Automated Runtime-Aware Scheduling for Multi-Tenant DNN Inference on GPU
Real-Time DNN (not only focus on ADS) related research papers are listed in [Real-Time DNN](will be published soon)

E2E ADS Study

[RTSS'19] Predictable Data-Driven Resource Management: an Implementation using Autoware on Autonomous Platforms
[RTAS'20] Co-Optimizing Performance and Memory Footprint Via Integrated CPU/GPU Memory Management, an Implementation on Autonomous Driving Platform
[EuroSys'22] D3: a dynamic deadline-driven approach for building autonomous vehicles
[INFOCOM'22] AoI-centric Task Scheduling for Autonomous Driving Systems
[ICRA'21] Pylot: A Modular Platform for Exploring Latency-Accuracy Tradeoffs in Autonomous Vehicles
[IISWC'20] Demystifying Power and Performance Bottlenecks in Autonomous Driving Systems

Perception Module of ADS

[ICCD'20] Driving Scenario Perception-Aware Computing System Design in Autonomous Vehicles
[RTSS'20] R-TOD: Real-time object detector with minimized end-to-end delay for autonomous driving
[DAC'22] EcoFusion: energy-aware adaptive sensor fusion for efficient autonomous vehicle perception
[RTSS'22] RT-MOT: Confidence-Aware Real-Time Scheduling Framework for Multi-Object Tracking Tasks
[ICDCS'22] Multi-View Scheduling of Onboard Live Video Analytics to Minimize Frame Processing Latency
[ML4AD'19] End-to-End Delay Analysis and Optimization of Object Detection Module for Autonomous Driving
[RTAS'20] Real-Time Object Detection System with Multi-Path Neural Networks
[MobiSys'22] AutoCast: Scalable Infrastructure-less Cooperative Perception for Distributed Collaborative Driving
[CVPR'20] Towards Streaming Perception

Middleware: ROS2

[ECRTS'19] Response-Time Analysis of ROS 2 Processing Chains Under Reservation-Based Scheduling
[RTSS'22] Real-Time Scheduling and Analysis of Processing Chains on Multi-threaded Executor in ROS 2
[RTSS'22] Worst-Case Time Disparity Analysis of Message Synchronization in ROS
[RTAS'23] Real-Time Performance Analysis of Processing Systems on ROS 2 Executors
[RTCSA'23] Timing-Aware ROS 2 Architecture and System Optimization
[RTSS'22] End-To-End Timing Analysis in ROS2

Case Study Solution

[Autoware] [ADS Framework] (https://github.com/autowarefoundation/autoware/)
[AWSIM] [Simulator] (https://tier4.github.io/AWSIM/)

Acknowledgments

Thanks a lot for Dr. Liangkai Liu (https://sites.google.com/view/liangkai/home) and his awesome paper list Awesome-CAV-System-Paper although he doesn't know me.

This paper list is collected by members of Real-Time System Group of Memory-System-and-IOT-Lab (M Lab)(https://mlab.hk) from City University of Hong Kong.

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