3 citations · 5 across the 5 of their papers we have counts for
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cs.LG2023
Deep Occupancy-Predictive Representations for Autonomous Driving
Eivind Meyer, Lars Frederik Peiss, Matthias Althoff
Manually specifying features that capture the diversity in traffic environments is impractical. Consequently, learning-based agents cannot realize their full potential as neural mo…
cs.LG2023
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric
Eivind Meyer, Maurice Brenner, Bowen Zhang +3
Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants an…