Johannes Quast
AI & Robotics Solutions Engineer, Edge Customer Engineering (ECE) at Intel Corporation
Karlsruhe, Baden-Württemberg, Germany
Summary
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Top SchoolJohannes Quast is an AI and robotics solutions engineer with 12 years of experience building dependable embedded and automated driving systems, now focusing on edge customer engineering at Intel. His career at Intel and Intel Labs spans research and product delivery across industrial, automotive, and robotics domains, blending deep embedded software expertise with safety-focused features like Responsibility Sensitive Safety. He has contributed to the widely used CARLA autonomous driving simulator, enhancing vehicle physics, transmission details, and RSS support—an indicator of practical impact on simulation-driven development. Based in Karlsruhe and trained in computer science at KIT, he brings a hands-on systems mindset that connects low-level vehicle dynamics to deployable edge AI solutions.
12 years of coding experience
18 years of employment as a software developer
Diplom Informatik, Diplom Informatik at Karlsruhe Institute of Technology (KIT)
English, French, German
Stackoverflow
101reputation
0reached
0answers
0questions
Github Skills (11)
c-language10
physics10
cprogramming-language10
unreal-engine-410
simulator10
autonomous-driving10
carla10
python9
apidoc9
api9
ai8
Programming languages (5)
Github contributions (5)
carla-simulator/carla
Feb 2019 - Jul 2019
Open-source simulator for autonomous driving research.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits, 10 PRs, 22 pushes in 4 months
Contributions summary:Johannes primarily focused on modifying and extending the LibCarla library, specifically addressing functionalities related to vehicle physics, transmission, and Responsibility Sensitive Safety (RSS). Their contributions included adding access to vehicle brake and handbrake torque values, introducing transmission gear values and forward gear details. Moreover, the user implemented features to add RSS functionalities to the client library.
deep-reinforcement-learningsimulationcarlasimulatorsimulator-source
johannesquast/carla-autoware
Feb 2019 - Mar 2019
Integration of AutoWare AV software with the CARLA simulator
Contributions:3 pushes, 4 branches in 1 month
autowaresimulationsimulatorcarlacarla-simulator
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