13 Computer Vision News Traditionally, planning modules were rule-based, requiring handcrafted rules for every possible scenario. While these methods served their purpose, they lacked flexibility and scalability, making them challenging to adapt to dynamic and complex real-world environments. Previous works perform this learning-based planning using a rendered image and a CNN or a graph or object-level representation evaluated offline. Katrin’s innovative model performs online evaluations of the ego vehicle’s trajectory in real-time simulations, a vital improvement on offline metrics. Other key aspects that set PlanT apart are its simplicity and extendibility. Many existing models use complicated architectures, making implementation and understanding difficult. In contrast, PlanT offers a straightforward yet effective transformer-based approach that can serve as a baseline for various extensions. It tokenizes the scene, making it easily adaptable for other use cases, such as inputting language tokens for combining language and driving. A transformer network inherently has attention weights. Katrin used this feature to enhance the explainability of the model. Attention weights were visualized to determine how much attention the network assigned to each vehicle in the scene when making decisions. Additionally, she proposed an evaluation scheme to assess the reliability of these attention weights to explain the model’s decisions, as she tells us there is some contention in the field around whether attention is explainability. What does Katrin think convinced the jury to award her presentation the top prize? “There was a first, second, and third place, and from what I saw of the others, I think we had the most nonstandard poster,” she reveals. “The posters were not what you see all the time at poster sessions. All three had some kind of fancy layout. A third of my poster was just a red bar with a title, my logo from the project, and the QR code. I had a smaller piece for the content with the really important stuff. It was an eye-catcher. Presentation-wise, many people did a great job conveying what they did and explaining it in a clear way.” Check out our video interview with Katrin to learn more about this work, including the efforts that have already begun to extend it, her thoughts on modular vs end-to-end approaches, and her unconventional path to working in this domain. Katrin Renz
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