Master Thesis Sampling-based Prediction and Planning for Autonomous Driving
Sep 13, 2023
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The Robert Bosch GmbH is looking forward to your application!
Anticipating the future behavior of surrounding traffic participants is a crucial enabler for safe and performant autonomous driving in challenging urban scenarios. It enables a downstream planner to avoid collisions and progress toward its goal while interacting with other traffic participants.
In this thesis we want to integrate a probabilistic prediction module with an interactive planning component, benchmark and evaluate the system in simulations and on research datasets, and extend it to address limitations in the state of the art. In particular, the proposed integration aims to address highly interactive scenarios in which the ego must decide if, how and when to influence the other road users.
- In the scope of your research, you will implement and evaluate an integrated self-driving system based on available components, e.g. sampling-based prediction and planning.
- Moreover, you will investigate your approaches on realistic open-source benchmarks and extend the integrated system to appropriately trade-off defensiveness and assertiveness.
- Later, you will be able to develop and test additional improvements on your own and discuss them within the scientific community to receive valuable feedback.
- Last but not least you will conduct independent research and present results in publishable manner.
- Education: studies in the field of Computer Science, Electrical Engineering or comparable with a robotics/machine learning focus and very good grades
- Experience and Knowledge: Experience in reading research papers and coding experience for machine learning applications, in Python with PyTorch, TensorFlow or JAX
- Personality and Working Practice: eager to learn and open-minded to publish new findings
- Enthusiasm: to dive into a topic at the frontiers of machine learning research and autonomous driving applications
- Languages: fluent in English
Start: according to prior agreement
Duration: 9 months
Requirement for this thesis is the enrollment at university. Please attach your CV, transcript of records, examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore, we welcome all applications, regardless of gender, age, disability, religion, ethnic origin or sexual identity.
Need further information about the job?
Faris Janjos (Business Department)
+49 711 811 49109
Marcel Hallgarten (Business Department)
+49 711 811 58842