Connected Dependability Cage Approach for Safe Automated Driving

• URL: http://arxiv.org/abs/2307.06258v1
• Date: Wed, 12 Jul 2023 15:55:48 GMT
• Title: Connected Dependability Cage Approach for Safe Automated Driving
• Authors: Adina Aniculaesei, Iqra Aslam, Daniel Bamal, Felix Helsch, Andreas Vorwald, Meng Zhang and Andreas Rausch
• Abstract summary: This paper presents a safety concept for automated driving systems. It uses a combination of onboard runtime monitoring via connected dependability cage and off-board runtime monitoring via a remote command control center. We evaluate our safety concept for automated driving systems in a lab environment and on a test field track and report on results and lessons learned.
• Score: 2.369782235753731
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated driving systems can be helpful in a wide range of societal challenges, e.g., mobility-on-demand and transportation logistics for last-mile delivery, by aiding the vehicle driver or taking over the responsibility for the dynamic driving task partially or completely. Ensuring the safety of automated driving systems is no trivial task, even more so for those systems of SAE Level 3 or above. To achieve this, mechanisms are needed that can continuously monitor the system's operating conditions, also denoted as the system's operational design domain. This paper presents a safety concept for automated driving systems which uses a combination of onboard runtime monitoring via connected dependability cage and off-board runtime monitoring via a remote command control center, to continuously monitor the system's ODD. On one side, the connected dependability cage fulfills a double functionality: (1) to monitor continuously the operational design domain of the automated driving system, and (2) to transfer the responsibility in a smooth and safe manner between the automated driving system and the off-board remote safety driver, who is present in the remote command control center. On the other side, the remote command control center enables the remote safety driver the monitoring and takeover of the vehicle's control. We evaluate our safety concept for automated driving systems in a lab environment and on a test field track and report on results and lessons learned.

Related papers

• Efficient Mixture-of-Expert for Video-based Driver State and Physiological Multi-task Estimation in Conditional Autonomous Driving [12.765198683804094]
  Road safety remains a critical challenge worldwide, with approximately 1.35 million fatalities annually attributed to traffic accidents. We propose a novel multi-task DMS, termed VDMoE, which leverages RGB video input to monitor driver states non-invasively.
  arXiv  Detail & Related papers  (2024-10-28T14:49:18Z)
• Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System through Distributed Database and Multimodal Perception: Demonstrated in Crossroads [11.623582669220115]
  This paper introduces a novel Real-time Vehicle-to-Vehicle Communication Based Network Cooperative Control System (VVCCS) VVCCS revolutionizes macro-scope traffic planning and collision avoidance in autonomous driving. We also developed a comprehensive multi-modal perception system with multi-objective tracking and radar sensing.
  arXiv  Detail & Related papers  (2024-10-23T05:59:55Z)
• Empowering Autonomous Driving with Large Language Models: A Safety Perspective [82.90376711290808]
  This paper explores the integration of Large Language Models (LLMs) into Autonomous Driving systems. LLMs are intelligent decision-makers in behavioral planning, augmented with a safety verifier shield for contextual safety learning. We present two key studies in a simulated environment: an adaptive LLM-conditioned Model Predictive Control (MPC) and an LLM-enabled interactive behavior planning scheme with a state machine.
  arXiv  Detail & Related papers  (2023-11-28T03:13:09Z)
• Classification of Safety Driver Attention During Autonomous Vehicle Operation [11.33083039877258]
  This paper introduces a dual-source approach integrating data from an infrared camera facing the vehicle operator and vehicle perception systems. The proposed system effectively determines a metric for the attention levels of the vehicle operator, enabling interventions such as warnings or reducing autonomous functionality as appropriate.
  arXiv  Detail & Related papers  (2023-10-17T22:04:42Z)
• Runtime Safety Assurance of Autonomous Vehicles used for Last-mile Delivery in Urban Environments [0.10499611180329801]
  The project LogiSmile aims to find a possible solution to the challenges of urban goods distribution in congested areas. The Autonomous Hub Vehicle (AHV) works in cooperation with a small robot called the Autonomous Delivery Device (ADD) The runtime monitoring concept developed for the AHV has been demonstrated in 2022 in Hamburg.
  arXiv  Detail & Related papers  (2023-07-10T10:06:55Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Monitoring ROS2: from Requirements to Autonomous Robots [58.720142291102135]
  This paper provides an overview of a formal approach to generating runtime monitors for autonomous robots from requirements written in a structured natural language. Our approach integrates the Formal Requirement Elicitation Tool (FRET) with Copilot, a runtime verification framework, through the Ogma integration tool.
  arXiv  Detail & Related papers  (2022-09-28T12:19:13Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• Differentiable Control Barrier Functions for Vision-based End-to-End Autonomous Driving [100.57791628642624]
  We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving. We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
  arXiv  Detail & Related papers  (2022-03-04T16:14:33Z)
• A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge [49.976633450740145]
  This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
  arXiv  Detail & Related papers  (2020-10-23T18:07:48Z)
• Development of a Fuzzy-based Patrol Robot Using in Building Automation System [1.5293427903448025]
  The paper presents a novel security system in which a mobile robot is used as a patrol. The robot is equipped with fuzzy-based algorithms to allow it to avoid the obstacles in an unknown environment. The experiment results show that the system satisfies the requirements for the objective of monitoring and securing the building.
  arXiv  Detail & Related papers  (2020-05-13T07:05:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.