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Zeekr Technology Europe
Thesis - Edge-Deployed Off-Road Terrain Recognition for...
Gothenburg
October 21, 2024
We are Zeekr Technology Europe. Powered by engineers and creative minds from all over the world. Located in Gothenburg, Sweden, one of the leading mobility innovation grounds worldwide. Established in 2013 as CEVT and began with the mission to create a revolutionary modular vehicle architecture, CMA. As of 2024, CEVT is now Zeekr Technology Europe, signaling our belonging within Zeekr. Delivering intelligent, sustainable, and user centric technology and software for tomorrow's electric mobility today.
Project Description:
The increasing demand for enhanced off-road vehicle navigation has sparked significant interest in developing sophisticated driver assistance systems. Accurate classification of off-road terrain, including its properties such as roughness and unevenness, is crucial for improving vehicle performance, safety, and navigation efficiency. By leveraging sensor data from various sources, including wheel speed, Inertial Measurement Units (IMU), acceleration, brake pedal data, suspension travel, and odometry, it is possible to develop robust models that can classify different types of off-road terrain and their characteristics.
Scope
The primary objective of this master thesis is to develop a method for classifying off-road terrain and its properties, such as roughness and unevenness, using a dataset of labeled off-road driving data. The dataset includes labels for various types of off-road terrain, including grass, gravel, and rock, along with sensor data from wheel speed, IMU, acceleration, brake pedal usage, suspension travel, and odometry.
Objectives
The expected outcomes of this thesis include the development of a robust and accurate terrain classification model that can identify off-road terrain properties such as roughness and unevenness, successful deployment of this model on edge devices within vehicles, and validation of the model's performance in real-world off-road environments. This work aims to enhance the capabilities of off-road vehicles, improving their performance, safety, and navigation efficiency
Why you should join Zeekr Tech Eu
We are engineers, developers, and innovators from around the world. Joined together by entrepreneurship, our unique blend of global culture, and a belief in a smarter more sustainable future. At Zeekr Tech Eu we fast-track innovation and transform ideas into pioneering technology solutions, doing your master thesis here is no different. We are convinced that a thesis project is a major contribution to our innovation capabilities and long-term development. You'll have a great opportunity to use your skills and creativity to push the boundaries of what´s possible.
What happens when you apply
If this sounds interesting and you match the requirements, please don't hesitate to submit your application with a CV and cover letter. Shortlisted candidates will be contacted for an interview to further discuss the project's details and expectations.
Don't hesitate to get in touch with the supervisors for more information about the project:
Last application date: 2024-11-06
Apply today. We will perform ongoing selection during the application period. We look forward to hearing from you!
Please note that due to GDPR regulations, we can only accept applications sent through the recruitment system, not via email or other channels.
Project Description:
The increasing demand for enhanced off-road vehicle navigation has sparked significant interest in developing sophisticated driver assistance systems. Accurate classification of off-road terrain, including its properties such as roughness and unevenness, is crucial for improving vehicle performance, safety, and navigation efficiency. By leveraging sensor data from various sources, including wheel speed, Inertial Measurement Units (IMU), acceleration, brake pedal data, suspension travel, and odometry, it is possible to develop robust models that can classify different types of off-road terrain and their characteristics.
Scope
The primary objective of this master thesis is to develop a method for classifying off-road terrain and its properties, such as roughness and unevenness, using a dataset of labeled off-road driving data. The dataset includes labels for various types of off-road terrain, including grass, gravel, and rock, along with sensor data from wheel speed, IMU, acceleration, brake pedal usage, suspension travel, and odometry.
Objectives
- Data Preprocessing: Clean and prepare the dataset for training machine learning models.
- Feature Extraction: Identify and extract key features from sensor data for accurate terrain classification, including terrain properties such as roughness and unevenness.
- Model Development: Train machine learning models using the processed data to classify off-road terrain types and their characteristics.
- Model Evaluation: Assess the models with standard metrics and validate their classification accuracy.
- Edge Deployment: Investigate deploying the trained model on a microcontroller in the vehicle, considering computational limits and real-time processing needs, ensuring scalability across various vehicles.
The expected outcomes of this thesis include the development of a robust and accurate terrain classification model that can identify off-road terrain properties such as roughness and unevenness, successful deployment of this model on edge devices within vehicles, and validation of the model's performance in real-world off-road environments. This work aims to enhance the capabilities of off-road vehicles, improving their performance, safety, and navigation efficiency
- Master’s degree in Computer Science, Vehicle Engineering, Physics, or related fields
- Strong programming skills in Python, C++, or similar languages
- Experience with machine learning frameworks (e.g., Tensor Flow, Py Torch)
- Strong background in Machine Learning and Data Science, with experience in working with time-series data, training, and evaluating classification models.
- Knowledge of sensor data processing and feature extraction techniques.
- Familiarity with Embedded Systems and Edge Computing, including experience with microcontroller programming and deployment.
- Strong analytical and problem-solving skills.
Why you should join Zeekr Tech Eu
We are engineers, developers, and innovators from around the world. Joined together by entrepreneurship, our unique blend of global culture, and a belief in a smarter more sustainable future. At Zeekr Tech Eu we fast-track innovation and transform ideas into pioneering technology solutions, doing your master thesis here is no different. We are convinced that a thesis project is a major contribution to our innovation capabilities and long-term development. You'll have a great opportunity to use your skills and creativity to push the boundaries of what´s possible.
What happens when you apply
If this sounds interesting and you match the requirements, please don't hesitate to submit your application with a CV and cover letter. Shortlisted candidates will be contacted for an interview to further discuss the project's details and expectations.
Don't hesitate to get in touch with the supervisors for more information about the project:
- Karthik Prasad, karthik.prasad@zeekrtech.eu
- Utsav Khan, utsav.khan@zeekrtech.eu
Last application date: 2024-11-06
Apply today. We will perform ongoing selection during the application period. We look forward to hearing from you!
Please note that due to GDPR regulations, we can only accept applications sent through the recruitment system, not via email or other channels.
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