18 citations · 50 across the 8 of their papers we have counts for
8 papers
Tokenize the World into Object-level Knowledge to Address Long-tail Events in Autonomous Driving
Ran Tian, Boyi Li, Xinshuo Weng +5
The autonomous driving industry is increasingly adopting end-to-end learning from sensory inputs to minimize human biases in system design. Traditional end-to-end driving models, h…
Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps
Katie Z Luo, Xinshuo Weng, Yan Wang +5
Autonomous driving has traditionally relied heavily on costly and labor-intensive High Definition (HD) maps, hindering scalability. In contrast, Standard Definition (SD) maps are m…
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
Jiawei Yang, Boris Ivanovic, Or Litany +8
We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes. Grounded in neural fields, EmerNeRF simultaneously capt…
Language Conditioned Traffic Generation
Shuhan Tan, Boris Ivanovic, Xinshuo Weng +2
Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment…
Task-Aware Risk Estimation of Perception Failures for Autonomous Vehicles
Pasquale Antonante, Sushant Veer, Karen Leung +3
Safety and performance are key enablers for autonomous driving: on the one hand we want our autonomous vehicles (AVs) to be safe, while at the same time their performance (e.g., co…
Tree-structured Policy Planning with Learned Behavior Models
Yuxiao Chen, Peter Karkus, Boris Ivanovic +2
Autonomous vehicles (AVs) need to reason about the multimodal behavior of neighboring agents while planning their own motion. Many existing trajectory planners seek a single trajec…