I work as an Autonomous Driving Research Engineer, and my passion lies in making autonomous vehicles safer. I've dived deep into the world of Machine Learning, during my academic journey at the Cooperative State University, the Technical University of Munich, Delft University of Technology and the University of California in San Diego. Using my programming skills I love to solve hard research problems and develop new approaches. My strength lies in understanding the scene around self-driving cars, especially in 3D Object Detection. I have got hands-on experience dealing with sensor data at the the sensor data fusion department of Audi, where I have contributed to generate accurate ground truth datasets. Additionally, I got accepted for a visiting scholar position abroad at the Laboratory of Intelligent and Safe Automobiles (LISA) in San Diego (UCSD). Here, I have developed a 3D annotation toolbox to label LiDAR and camera data of a Tesla Model S, showcasing my commitment to pushing boundaries of this technology. Technical skills:
Electric, connected and automated. The transformation of road traffic wants to be actively shaped and contribute to increasing safety and efficiency for all road users through innovations. The AUTOtech.agil consortium project aims to create an open software and electrical/electronic architecture for the future mobility system. The special focus is on the standardization of interfaces as well as modularization with the aim of reusability, updatability, and expandability of individual functional modules. This modular principle of all necessary software and hardware elements for vehicles of all types makes it easy to implement additions and extensions in research, development, production, and, above all, in the utilization phase. The architecture for driverless vehicles researched and developed in the predecessor project UNICARagil is being extended to the entire transportation system, especially in the areas of software and tools for software development. Infrastructure-based sensor technology and cooperative concepts with control rooms and clouds are also being researched in depth. Three exemplary applications are used to demonstrate and implement the concepts with social value 1) Mobility for people with age- or illness-related performance limitations. 2) The sustainable transport of critical goods such as medicines. 3) A "guardian angel function" for greater safety of vulnerable road users, for example those on foot or by bicycle. Successful achievement of the project goal is only possible through interdisciplinary cooperation between leading partners from research with 17 chairs at nine universities and industry with three SMEs and nine companies in the field of automated and connected driving. The research project is funded by the German Federal Ministry of Education and Research (BMBF) under the "Electronics and Software Development Methods for the Digitalization of Automobility (Mannheim)" funding guideline (see project sheet DLR project management organization).
The "Providentia++" project by the Technical University of Munich aims to advance autonomous driving in mixed traffic conditions. The goal is to optimize traffic flow, reduce accidents, and leverage data collected by autonomous vehicles for commercial and scientific purposes. The project focuses on addressing challenges in complex traffic scenarios, such as intersections and roundabouts, where the limited field of vision of autonomous vehicles is insufficient. The project extends the existing infrastructure from the A9 motorway to urban areas in Garching-Hochbrück, aiming to test and research the interaction between autonomous vehicles and infrastructure in city traffic. To enhance system reliability, the project involves merging data streams from autonomous vehicles and infrastructure, optimizing algorithms for evaluating traffic situations, and ensuring robust operation in changing environmental conditions. "Providentia++" also emphasizes the development of a platform providing real-time traffic data, offering a uniform interface for commercial and scientific third-party software. The project is executed by a consortium of research institutes and industrial partners, spanning robotics, artificial intelligence, software technology, automotive, communication technology, and semiconductor technology.
Having more than 15 years of experience in Software Development, I am confident with Software Architecture, Object Oriented Design, Design Patterns, Test Driven Development (TDD), Continuous Integration and Delivery (CI/CD).
I am experienced Machine Learning research engineer with more than nine years of experience. Developing novel architectures, optimizing algorithms through hyperparameter tuning, training and evaluating machine learning models is one of my key strengths.
Experienced with data science techniques from storage of large data, transforming/converting data, curating ground truth datasets, building models (Python, PyTorch) to the visualization of data (open3d).
I have more than eight years of experience in developing autonomous driving software from perception (3D object detection) to prediction. I am expert in working with sensor data, creating ground truth datasets, and developing machine learning models for autonomous driving.
I can quickly analyze new information, find solutions and make decisions.
I am very passioned about the work I do which makes me to deliver great results in a short time.
I have experience in leading projects at university and acquired my skills in seminars and university certification programs.
Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas Festag, Alois Knoll
Ahmed Ghita, Bjørk Antoniussen, Walter Zimmer, Ross Greer, Christian Creß, Andreas Møgelmose, Mohan M. Trivedi, Alois C. Knoll
Christian Creß, Walter Zimmer, Nils Purschke, Bach Ngoc Doan, Venkatnarayanan Lakshminarasimhan, Leah Strand, Alois C Knoll
Xinyi Li, Zijian Ma, Yinlong Liu, Walter Zimmer, Hu Cao, Feihu Zhang, Alois Knoll
Walter Zimmer, Christian Creß, Huu Tung Nguyen, Alois C Knoll
Walter Zimmer, Joseph Birkner, Marcel Brucker, Huu Tung Nguyen, Stefan Petrovski, Bohan Wang, Alois C Knoll
Walter Zimmer, Jialong Wu, Xingcheng Zhou, Alois C Knoll
Raphael van Kempen, Walter Zimmer, Christian Creß, Xingcheng Zhou, ..., Alois Knoll, Lutz Eckstein
Aral Hekimoglu, Philipp Friedrich, Walter Zimmer, Michael Schmidt, Alvaro Marcos-Ramiro, Alois Knoll
Christian Creß, Walter Zimmer, Leah Strand, Maximilian Fortkord, Siyi Dai, Venkatnarayanan Lakshminarasimhan, Alois Knoll
Walter Zimmer, Marcus Grabler, Alois Knoll
Walter Zimmer, Emec Ercelik, Xingcheng Zhou, Xavier Jair Diaz Ortiz, Alois Knoll
Best Student Paper Award at the IEEE Intelligent Transportation Systems Conference (ITSC) 2023
1. Place at one of 10 challenges, 121 teams. Road Rendering Challenge Winner Award from Autonomous Intelligent Driving (AID). Price includes visiting a conference.
Within the best 28% of master students (after 3rd master semester). Passed 7 exams (+41 ECTS) in addition.
Research stay abroad at the University of California, UCSD, San Diego, USA (16% selection rate)
Within the best 10-20% of students (same year of graduation)
Within the best 20% of students (same year of graduation)
From simulation to real-world testing. Learn how to validate ADAS functions and systems. Understand the requirements for ADAS validation and the methods for testing and validation. Get to know the challenges of ADAS validation and the methods for testing and validation.
Develop an understanding of MLOps and the required steps to bring a reproducible and traceable ML model to production including an example project using open-source MLOps tools.
Work with the pinhole camera model, and perform intrinsic and extrinsic camera calibration. Detect, describe and match image features and design your own convolutional neural networks. Apply these methods to visual odometry, object detection and tracking. Apply semantic segmentation for drivable surface estimation.
Understand commonly used hardware used for self-driving cars. Identify the main components of the self-driving software stack. Program vehicle modelling and control. Analyze the safety frameworks and current industry practices for vehicle development.
The seminar included contents from the following domains 1) Introduction to Agile Practices. 2) The Scrum Framework 2.1) Scrum Artifacts 2.2) The Scrum Team 2.3) Scrum Events 2.4) Further Topics 3) Sample Exam Questions and Exercises
Analyzing and evaluating sample papers. Reflecting on your own writing process. Making critical judgements on your own writing. Creating a repertoire of effective self-editing tools.
Learn how to set and achieve your goals.
Advanced C++ training on object-oriented (OO) software design with the C++ programming language. The focus of the training is put on the essential OO and C++ software development principles, concepts, idioms, and best practices, which enable programmers to create professional, high-quality code. The course teaches guidelines to develop mature, robust, and maintainable C++ code and combines lectures and hands-on sessions.
Attending the IV'20 conference (remote).
Presenting research work (3D Labeling Tool) at the IV'19 conference
Certified in entrepreneurship.
Certified in process and quality improvement.
Training for becoming a leader (know your strengths)
Training for acquiring leadership skills
Build relationship, train intercultural communication, lead difficult conversations
14 months long interdisciplinary project in Space Mission Operations. Implementation of a web user interface for the Mission Control Center for the satellite mission MOVE-II. Development of a web admin dashboard for controlling the satellite, create visualizations (Web Development, JavaScript, TypeScript, AngularJS, NodeJS, d3.JS)
DAAD English language certificate, C1 level
Build intercultural communication skills
TUM Hackathon (HackaTUM). Develop and present a prototype to several companies. Smart Garage Door (2016), roads.sexy (2019, winner)
International semester in the area of Automation Systems Engineering. Focus on Extended Concepts in Automation, Simulative Engineering, Embedded Systems. Passed 12 exams.
Certified trainer (passed two exams).
Certified Java Application Developer
+49 (89) 289 - 18104
k@tum.de
858-822-0075
mtrivedi@ucsd.edu