1 citations · 1 across the 6 of their papers we have counts for
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Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
Abhishek Vivekanandan, Ahmed Abouelazm, J. Marius Zöllner
Motion forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries that are highly prone to laten…
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
Abhishek Vivekanandan, Christian Hubschneider, J. Marius Zöllner
The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion…
Generative AI for Autonomous Driving: A Review
Katharina Winter, Abhishek Vivekanandan, Rupert Polley +17
Generative AI (GenAI) is rapidly advancing the field of Autonomous Driving (AD), extending beyond traditional applications in text, image, and video generation. We explore how gene…
RetroMotion: Retrocausal Motion Forecasting Models are Instructable
Royden Wagner, Omer Sahin Tas, Felix Hauser +7
Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint tr…
Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach
Abhishek Vivekanandan, J. Marius Zöllner
Representing diverse and plausible future trajectories is critical for motion forecasting in autonomous driving. However, efficiently capturing these trajectories in a compact set…
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization
Abhishek Vivekanandan, Niels Maier, J. Marius Zoellner
Environmental perception obtained via object detectors have no predictable safety layer encoded into their model schema, which creates the question of trustworthiness about the sys…