Topological Road Mapping for Autonomous Driving Applications

Murciego, Esther; Huélamo, Carlos Gómez; Barea, Rafael; Bergasa, Luis M.; Romera, Eduardo; Arango, J. Felipe; Tradacete, Miguel; Sáez, A. Bernabé

doi:10.1007/978-3-319-99885-5_18

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Fisheye camera was integrated in an open-source autonomous car prototype [33,34] as a complement to its main perception system, formed by a ZED camera, manufactured by StereoLabs, a VLP-16 LiDAR, manufactured by Velodyne, a HiPer Pro RTK-GPS receiver by TOPCON and odometry sensors by Kubler. Environment perception of the prototype is based on 3D semantic segmentation obtained from the fusion of LiDAR and segmented images, which is able to detect obstacles in a 3D environment [35].…”

Section: Application To a Real Fisheye Cameramentioning

confidence: 99%

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

Sáez

Bergasa

López-Guillén

et al. 2019

Sensors

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The interest in fisheye cameras has recently risen in the autonomous vehicles field, as they are able to reduce the complexity of perception systems while improving the management of dangerous driving situations. However, the strong distortion inherent to these cameras makes the usage of conventional computer vision algorithms difficult and has prevented the development of these devices. This paper presents a methodology that provides real-time semantic segmentation on fisheye cameras leveraging only synthetic images. Furthermore, we propose some Convolutional Neural Networks(CNN) architectures based on Efficient Residual Factorized Network(ERFNet) that demonstrate notable skills handling distortion and a new training strategy that improves the segmentation on the image borders. Our proposals are compared to similar state-of-the-art works showing an outstanding performance and tested in an unknown real world scenario using a fisheye camera integrated in an open-source autonomous electric car, showing a high domain adaptation capability.

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Section: Application To a Real Fisheye Cameramentioning

confidence: 99%

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

Sáez

Bergasa

López-Guillén

et al. 2019

Sensors

Self Cite

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Train Here, Drive There: Simulating Real-World Use Cases with Fully-Autonomous Driving Architecture in CARLA Simulator

Gómez-Huélamo

Egido

Bergasa

et al. 2020

Advances in Intelligent Systems and Computing

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Train here, drive there: ROS based end-to-end autonomous-driving pipeline validation in CARLA simulator using the NHTSA typology

Gómez-Huélamo

Egido

Bergasa

et al. 2021

Multimed Tools Appl

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Urban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. In this paper we present a validation of our fully-autonomous driving architecture using the NHTSA (National Highway Traffic Safety Administration) protocol in the CARLA simulator, focusing on the analysis of our decision-making module, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, the paper states the importance of using hyper-realistic simulators, as a preliminary help to real test, as well as an appropriate design of the traffic scenarios as the two current keys to build safe and robust AD technology. Second, our pipeline is introduced, which exploits the concepts of standard communication in robotics using the Robot Operating System (ROS) and the Docker approach to provide the system with isolation, flexibility and portability, describing the main modules and approaches to perform the navigation. Third, the CARLA simulator is described, outlining the steps carried out to merge our architecture with the simulator and the advantages to create ad-hoc driving scenarios for use cases validation instead of just modular evaluation. Finally, the architecture is validated using some challenging driving scenarios such as Pedestrian Crossing, Stop, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files: Simulation Use Cases) and quantitative (linear velocity and trajectory splitted in the corresponding HIBPN states) results are presented for each use case, as well as an analysis of the temporal graphs associated to the Vulnerable Road Users (VRU) cases, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.

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Topological Road Mapping for Autonomous Driving Applications

Cited by 5 publications

References 6 publications

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

Train Here, Drive There: Simulating Real-World Use Cases with Fully-Autonomous Driving Architecture in CARLA Simulator

Train here, drive there: ROS based end-to-end autonomous-driving pipeline validation in CARLA simulator using the NHTSA typology

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