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Name: Klemens Esterle

Type: User

Company: BMW Group

Location: Munich, Germany

Blog: https://www.linkedin.com/in/klemens-esterle/

Hello there! 👋

I'm a software engineer working on autonomous vehicles located in Munich, Germany.

BIO

🚗 I focus on developing, simulating and verifying planning algorithms for autonomous driving
🏢 I'm currently working at BMW Group
🔭 I have a Ph.D. in computer science from the Technical University of Munich 🎓
🔭 I have a diploma in mechatronics engineering from the Technical University Dresden 🎓
⚙️ I use daily: C++

HOW TO REACH ME

CHECK OUT MY PUBLICATIONS

MONA: The Munich Motion Dataset of Natural Driving (ITSC 2022)
Mixed-Integer Motion Planning on German Roads within the Apollo Driving Stack (Transactions on Intelligent Vehicles 2022)
BARK: Open Behavior Benchmarking in Multi-Agent Environments (IROS 2020)
Formalizing Traffic Rules for Machine Interpretability (CAVS 2020)
Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning (IROS 2020, PLC Workshop)
Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation (IV 2020)
Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming (CDC 2020)
From Specifications to Behavior: Maneuver Verification in a Semantic State Space (IV 2019)
Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving (IV 2019)
Spatiotemporal Motion Planning with Combinatorial Reasoning for Autonomous Driving (ITSC 2018)
Roadgraph Generation and Free-Space Estimation in Unknown Structured Environments for Autonomous Vehicle Motion Planning (IV 2018)
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning (IV 2018)

CHECK OUT MY OPEN SOURCE WORK ⬇️

Klemens Esterle's Projects

bark

Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving

bark-ml

Gym environments and agents for autonomous driving.

benchmark-database

Reproducible Scenario-Database for Autonomous Driving

carnd-extended-kalman-filter-project

Self-Driving Car Nanodegree Program Starter Code for the Extended Kalman Filter Project

carnd-functional-safety-project

carnd-kidnapped-vehicle-project

carnd-lanelines-p1

Lane Finding Project for Self-Driving Car ND

carnd-mpc-project

CarND Term 2 Model Predictive Control (MPC) Project

carnd-path-planning-project

Create a path planner that is able to navigate a car safely around a virtual highway

carnd-traffic-sign-classifier-project

Classify Traffic Signs.

carnd-unscented-kalman-filter-project

Self-Driving Car Nanodegree Program Starter Code for the Unscented Kalman Filter Project

cppnd-memory-management-chatbot

eigen-git-mirror

THIS MIRROR IS DEPRECATED -- New url: https://gitlab.com/libeigen/eigen

The gflags package contains a C++ library that implements commandline flags processing. It includes built-in support for standard types such as string and the ability to define flags in the source file in which they are used. Online documentation available at: