Amit Chaulwar successfully completes his doctorate as the first fellow of the BayWISS Collaborative Research Center Mobility and Transport

Image of Amit Chaulwar with a multi-lane road in the background.
Screenshot of the online defense. Pictured is the dissertation topic, the three examiners, and Amit Chaulwar.

Amit Chaulwar (top left) and the three examiners.

Mr. Amit Chaulwar joined the newly established BayWISS Verbundkolleg Mobility and Transport as one of the first PhD students in 2016. After 5 years, he defended his dissertation on "Hybrid Machine Learning Methods for Vehicle Safety Applications". The PhD at THI was done in cooperation with TUM, he was supervised by Prof. Dr.-Ing. Michael Botsch (THI) and Prof. Dr.-Ing. Wolfgang Utschick (TUM).

His work proposes hybrid machine learning methods for safe trajectory planning in critical traffic scenarios. Two new model-based algorithms, namely Augmented CL-RRT and Augmented CL-RRT+, are developed by extending the sampling-based Rapid-exploring Random Tree (RRT) algorithm and combined with machine learning techniques. These algorithms plan safe trajectories considering vehicle driving dynamics characteristics while conserving on-board limited computational resources.

Trajectory planning is a key task for autonomous mobility applications. A particular challenge is the safe trajectory planning for vehicles in critical traffic scenarios with multiple static and dynamic objects. In a critical traffic scenario, a safe trajectory should be planned to avoid a collision and if this is no longer possible, to mitigate the consequences.

We congratulate him warmly and wish him much success for his future career!