Digital Gate: Automotive Gateway to Automation Platforms

Ezz, Abdelrahman M.; Nabil, Ashraf; Ali, Waleed; Abdou, Mohammed; Azer, Mark; Farag, Ibrahim; Agamawi, Motaz

doi:10.1109/icict52872.2021.00036

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…Otherwise, in case of no crash, there will not be a trigger, so no action is taken. The proposed system is integrated with the digital gate platform [ 45 ]—let’s do platform (LDO) [ 49 ] proposed by Valeo, which is considered an automotive standardization platform as shown in Figure 10 .…”

Section: System Methodologymentioning

confidence: 99%

“…The comparative study was based on different comparison metrics related to integrability, accessibility, and integrability. Valeo proposed an automotive standardization platform called digital gate system [ 45 ] that aims to connect a vehicle’s network (including CAN, Flexray, Ethernet, etc.) to cloud computation platforms as well as to the automation platforms and vice versa.…”

Section: Literature Reviewmentioning

confidence: 99%

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SDC-Net: End-to-End Multitask Self-Driving Car Camera Cocoon IoT-Based System

Abdou¹,

Kamal

2022

Sensors

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Currently, deep learning and IoT collaboration is heavily invading automotive applications especially in autonomous driving throughout successful assistance functionalities. Crash avoidance, path planning, and automatic emergency braking are essential functionalities for autonomous driving. Trigger-action-based IoT platforms are widely used due to its simplicity and ability of doing receptive tasks accurately. In this work, we propose SDC-Net system: an end-to-end deep learning IoT hybrid system in which a multitask neural network is trained based on different input representations from a camera-cocoon setup installed in CARLA simulator. We build our benchmark dataset covering different scenarios and corner cases that the vehicle may expose in order to navigate safely and robustly while testing. The proposed system aims to output relevant control actions for crash avoidance, path planning and automatic emergency braking. Multitask learning with a bird’s eye view input representation outperforms the nearest representation in precision, recall, f1-score, accuracy, and average MSE by more than 11.62%, 9.43%, 10.53%, 6%, and 25.84%, respectively.

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Section: System Methodologymentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%