Autonomous Driving System : Model Based Safety Analysis

Tlig, Mohamed; Machin, Mathilde; Kerneis, Romain; Arbaretier, Emmanuel; Zhao, Linda Shuo; Meurville, Florent; Frank, Jean van

doi:10.1109/dsn-w.2018.00012

Cited by 12 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, scholars have also assessed the road trafc complexity, which is a description and assessment of the surrounding environmental conditions during driver driving and is closely related to the road trafc environment, mainly considering the static and dynamic environmental parameters [17,[28][29][30][31], with static including road facilities, weather, and other parameters and dynamic including the behavior of participants. Some studies integrated the static and dynamic parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Complexity of Potential Bicycle Interference on Vehicles on Urban Road Segments

Wang

2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

Interacting with bicycles on urban road segments is complex for vehicles, due to the diverse and flexible cycling behavior that can cause interferences. Studies show that driving mixed with bicycles is also a great challenge for autonomous vehicles (AVs), and it is necessary to consider the interference of bicycles when selecting public roads for testing. However, existing road evaluation methods mostly focus on autonomous driving functions and accident analysis, although bicycles have been considered, often with insufficient consideration of their interference. This study analyzes two types of cycling behavior that could interfere with vehicles, including lateral (turning handlebars) and longitudinal (braking or accelerating) behavior, with each occurrence of such behavior considered as one potential lateral or longitudinal interference. From the perspective of cycling behavior, a framework is proposed to assess the complexity of potential bicycle interference on vehicles on road segments. A higher frequency of both potential lateral and longitudinal interference represents a higher complexity of potential interference. A naturalistic field experiment was conducted to collect the potential lateral and longitudinal interference frequency and the environmental parameters of road segments. The quantile regression model was applied to analyze the environmental factors influencing different interference frequencies separately and further establish the assessing model of the potential bicycle interference complexity, and the usability of the model has been demonstrated with a case study. Results show that the potential interference complexity varies across road segments, with some factors leading to more frequent potential lateral and longitudinal interference but with varying degrees of impact (such as the separation between bicycles and vehicles), while some only affect the lateral interference frequency (such as the on-street parking condition). The proposed framework can help autonomous driving companies or evaluation agencies to select appropriate testing roads, thus promoting the development of autonomous driving.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Assessing the Complexity of Potential Bicycle Interference on Vehicles on Urban Road Segments

Wang

2023

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…In [35], a camera perception pipeline was used to quantify the safety of AVs. To work with models of whole autonomous driving stacks, Tlig et al [36] tried to model whole software stacks to build a safety concept upon. Almeaibed et al [37] used a digital twin approach for future safety considerations.…”

Section: E Safetymentioning

confidence: 99%

Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems

et al. 2023

View full text Add to dashboard Cite

The objective of this work is to provide a comprehensive understanding of the development of autonomous vehicle perception systems. So far, most autonomy perception research has been concentrated on improving perception systems' algorithmic quality or combining different sensor setups. In our work, we draw conclusions from participating in the Indy Autonomous Challenge 2021 and its follow-up event in Las Vegas 2022. These were the first head-to-head autonomous racing competitions that required an entire perception pipeline to perceive the environment and the opposing surrounding vehicles. Our research includes quantitative results from collected vehicle data and qualitative results from simulation, video, and multiple race analysis. The Indy Autonomous Challenge was one of the few research projects that considered the entire autonomous vehicle. Therefore, our findings indicate insights on the system level, including hardware setup and full-stack software. We can demonstrate that different sensor modalities in the vehicle have strengths and weaknesses when they are deployed. Our results further show the difficulties and challenges that emerge when multi-modal perception systems must run in real-time on real-world autonomous vehicles. The most concise finding from our investigation is the summary of critical learnings when developing and deploying perception systems for autonomous systems. Given the background of the study, it was inevitable that our conclusions were influenced by driving on the racetrack and only one hardware setup available. Therefore, in the discussion, we draw further parallels to driving on public roads in dense traffic. More studies are needed to investigate the development and deployment of multi-modal perception systems for autonomous road vehicles with different hardware setups and various object detection, localization, and prediction algorithms. The novel contributions of this work are given by 12 lessons learned, summarized in 5 categories. These were derived and validated through a realized real-world application project. The videos of the final events in Indianapolis and Las Vegas can be watched here: IAC: https://www.youtube.com/watch?v=ERTffn3IpIs&ab_channel=CNETHighlights AC@CES: https://www.youtube.com/watch?v=df9f4Qfa0uU&ab_channel=CNETHighlights Multiple modules of the software stack are open source: https://github.com/TUMFTM.INDEX TERMS Autonomous racing, autonomous vehicles, perception systems, software development.The associate editor coordinating the review of this manuscript and approving it for publication was Junho Hong . FIGURE 2. Main contribution of this work: the lessons learned presented.to develop, deploy, validate, and test a complete autonomous driving stack, from sensor data to vehicle actuation. Because of the challenge's tight schedule, software development had to begin before the vehicles were built. This made the simulation of the vehicles and the environment an important task to be successful in the final competition.The IAC took place on October 23, 2021, i...

show abstract

“…In the field of engineering construction and production, aiming at the autonomous platform for exploration and navigation in the tunnel network, Tlig et. al [76] proposed a system that allows two-mode including an exploration/investigation mode and autonomous navigation mode. In exploration/investigation mode, the remote management platform moves through the tunnel network, collects data, and links it to the 2D/3D map of the environment.…”

Section: Transformation Of Architecture From Automation System To Autonomous Systemmentioning

confidence: 99%

From Automation System to Autonomous System: An Architecture Perspective

Chen

Wen

Zhu

et al. 2021

JMSE

View full text Add to dashboard Cite

Autonomy is the core capability of future systems, and architecture design is one of the critical issues in system development and implementation. To discuss the architecture of autonomous systems in the future, this paper reviews the developing progress of architectures from automation systems to autonomous systems. Firstly, the autonomy and autonomous systems in different fields are summarized. The article classifies and summarizes the architecture of typical automated systems and infer three suggestions for building an autonomous system architecture: extensibility, evolvability, and collaborability. Accordingly, this paper builds an autonomous waterborne transportation system, and the architecture is composed of the object layer, cyberspace layer, cognition layer, and application layer, the proposed suggestions made in the construction of the architecture are reflected in the inter-relationships at all layers. Through the cooperation of four layers, the autonomous waterborne transportation system can autonomously complete the system functions, such as system control and transportation service. In the end, the characteristics of autonomous systems are concluded, from which the future primary research directions and the challenges of autonomous systems are provided.

show abstract

Autonomous Driving System : Model Based Safety Analysis

Cited by 12 publications

References 3 publications

Assessing the Complexity of Potential Bicycle Interference on Vehicles on Urban Road Segments

Assessing the Complexity of Potential Bicycle Interference on Vehicles on Urban Road Segments

Learn to See Fast: Lessons Learned From Autonomous Racing on How to Develop Perception Systems

From Automation System to Autonomous System: An Architecture Perspective

Contact Info

Product

Resources

About