A Self-Driving Car Architecture in ROS2

Reke, Michael; Peter, Daniel; Schulte-Tigges, Joschua; Schiffer, Stefan; Ferrein, Alexander; Walter, Thomas; Matheis, Dominik

doi:10.1109/saupec/robmech/prasa48453.2020.9041020

Cited by 64 publications

(18 citation statements)

References 11 publications

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“…The parameter space is shown in Table II. The message size and publishing rate ranges cover typical use-cases [29]. We selected a wide range of publishing rates because previous work has highlighted that higher publishing rates can lead to lower latencies as a result of the middleware implementation internal handling of messages [11].…”

Section: A Experiments Setupmentioning

confidence: 99%

ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

Bédard,

Lütkebohle,

Dagenais

2022

Preprint

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Testing and debugging have become major obstacles for robot software development, because of high system complexity and dynamic environments. Standard, middlewarebased data recording does not provide sufficient information on internal computation and performance bottlenecks. Other existing methods also target very specific problems and thus cannot be used for multipurpose analysis. Moreover, they are not suitable for real-time applications. In this paper, we present ros2 tracing, a collection of flexible tracing tools and multipurpose instrumentation for ROS 2. It allows collecting runtime execution information on real-time distributed systems, using the low-overhead LTTng tracer. Tools also integrate tracing into the invaluable ROS 2 orchestration system and other usability tools. A message latency experiment shows that the end-to-end message latency overhead, when enabling all ROS 2 instrumentation, is below 0.0055 ms, which we believe is suitable for production real-time systems. ROS 2 execution information obtained using ros2 tracing can be combined with trace data from the operating system, enabling a wider range of precise analyses, that help understand an application execution, to find the cause of performance bottlenecks and other issues. The source code is available at: https://gitlab.com/rostracing/ros2 tracing.

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Section: A Experiments Setupmentioning

confidence: 99%

ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

Bédard,

Lütkebohle,

Dagenais

2022

Preprint

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“…The AD SW installed in the CAV prototype is the ROS2based platform for self-driving cars developed by FH Aachen in cooperation with HMETC [25]. The use of ROS (Robot-Operating-System ) for self-driving car platforms became very popular as it facilitates flexible modular designs that can be adapted to various types of vehicles.…”

Section: ) Automated Driving Softwarementioning

confidence: 99%

Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles

Coll-Perales

Schulte-Tigges

Rondinone

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

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Automated driving is now possible in diverse road and traffic conditions. However, there are still situations that automated vehicles cannot handle safely and efficiently. In this case, a Transition of Control (ToC) is necessary so that the driver takes control of the driving. Executing a ToC requires the driver to get full situation awareness of the driving environment. If the driver fails to get back the control in a limited time, a Minimum Risk Maneuver (MRM) is executed to bring the vehicle into a safe state (e.g., decelerating to full stop). The execution of ToCs requires some time and can cause traffic disruption and safety risks that increase if several vehicles execute ToCs/MRMs at similar times and in the same area. This study proposes to use novel C-ITS traffic management measures where the infrastructure exploits V2X communications to assist Connected and Automated Vehicles (CAVs) in the execution of ToCs. The infrastructure can suggest a spatial distribution of ToCs, and inform vehicles of the locations where they could execute a safe stop in case of MRM. This paper reports the first field operational tests that validate the feasibility and quantify the benefits of the proposed infrastructure-assisted ToC and MRM management. The paper also presents the CAV and roadside infrastructure prototypes implemented and used in the trials. The conducted field trials demonstrate that infrastructure-assisted traffic management solutions can reduce safety risks and traffic disruptions.

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“…Some of such self-driving trains include the Docklands Light Railway (DLR) in London, UK [ 41 ], Yurikamome in Tokyo, Japan [ 40 ], London Heathrow airport’s ultra-pods [ 41 ] and SkyTrain in Vancouver, Canada [ 42 ]. The successor of Robot Operating System (ROS) ROS2 based self-driving vehicle architecture can activate safe and reliable real-time behavior [ 43 ]. Bakioglu et al [ 44 ] proposed VIKOR and TOPSIS algorithms to prioritize risks in self-driving cars, while another group of researchers [ 45 ] proposed a self-driving delivery robot in last-mile logistics.…”

Section: Related Workmentioning

confidence: 99%

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

Chy

Masum

Sayeed

et al. 2021

Sensors

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The rapid expansion of a country’s economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system’s IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system’s infrastructure is far too low-cost and easy to install.

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A Self-Driving Car Architecture in ROS2

Cited by 64 publications

References 11 publications

ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

Prototyping and Evaluation of Infrastructure-Assisted Transition of Control for Cooperative Automated Vehicles

Delicar: A Smart Deep Learning Based Self Driving Product Delivery Car in Perspective of Bangladesh

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