87 citations · 115 across the 13 of their papers we have counts for
16 papers
Noise-Aware Generative Microscopic Traffic Simulation
Vindula Jayawardana, Catherine Tang, Junyi Ji +3
Accurately modeling individual vehicle behavior in microscopic traffic simulation remains a key challenge in intelligent transportation systems, as it requires vehicles to realisti…
Wolf: Dense Video Captioning with a World Summarization Framework
Boyi Li, Ligeng Zhu, Ran Tian +20
We propose Wolf, a WOrLd summarization Framework for accurate video captioning. Wolf is an automated captioning framework that adopts a mixture-of-experts approach, leveraging comp…
Trajeglish: Traffic Modeling as Next-Token Prediction
Jonah Philion, Xue Bin Peng, Sanja Fidler
A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. In pursuit of this functionality, we apply tools fr…
Towards Viewpoint Robustness in Bird's Eye View Segmentation
Tzofi Klinghoffer, Jonah Philion, Wenzheng Chen +6
Autonomous vehicles (AV) require that neural networks used for perception be robust to different viewpoints if they are to be deployed across many types of vehicles without the rep…
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
Rafid Mahmood, James Lucas, David Acuna +6
Given a small training data set and a learning algorithm, how much more data is necessary to reach a target validation or test performance? This question is of critical importance…
MBEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation
Enze Xie, Zhiding Yu, Daquan Zhou +5
In this paper, we propose MBEV, a unified framework that jointly performs 3D object detection and map segmentation in the Birds Eye View~(BEV) space with multi-camera image inp…
Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior
Davis Rempe, Jonah Philion, Leonidas J. Guibas +2
Evaluating and improving planning for autonomous vehicles requires scalable generation of long-tail traffic scenarios. To be useful, these scenarios must be realistic and challengi…
Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation
David Acuna, Jonah Philion, Sanja Fidler
Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amou…
Learning Indoor Inverse Rendering with 3D Spatially-Varying Lighting
Zian Wang, Jonah Philion, Sanja Fidler +1
In this work, we address the problem of jointly estimating albedo, normals, depth and 3D spatially-varying lighting from a single image. Most existing methods formulate the task as…
DriveGAN: Towards a Controllable High-Quality Neural Simulation
Seung Wook Kim, Jonah Philion, Antonio Torralba +1
Realistic simulators are critical for training and verifying robotics systems. While most of the contemporary simulators are hand-crafted, a scaleable way to build simulators is to…
Emergent Road Rules In Multi-Agent Driving Environments
Avik Pal, Jonah Philion, Yuan-Hong Liao +1
For autonomous vehicles to safely share the road with human drivers, autonomous vehicles must abide by specific "road rules" that human drivers have agreed to follow. "Road rules"…
The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes
Yiluan Guo, Holger Caesar, Oscar Beijbom +2
A high-performing object detection system plays a crucial role in autonomous driving (AD). The performance, typically evaluated in terms of mean Average Precision, does not take in…
Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D
Jonah Philion, Sanja Fidler
The goal of perception for autonomous vehicles is to extract semantic representations from multiple sensors and fuse these representations into a single "bird's-eye-view" coordinat…
Learning to Simulate Dynamic Environments with GameGAN
Seung Wook Kim, Yuhao Zhou, Jonah Philion +2
Simulation is a crucial component of any robotic system. In order to simulate correctly, we need to write complex rules of the environment: how dynamic agents behave, and how the a…
Learning to Evaluate Perception Models Using Planner-Centric Metrics
Jonah Philion, Amlan Kar, Sanja Fidler
Variants of accuracy and precision are the gold-standard by which the computer vision community measures progress of perception algorithms. One reason for the ubiquity of these met…
FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network
Jonah Philion
The search for predictive models that generalize to the long tail of sensor inputs is the central difficulty when developing data-driven models for autonomous vehicles. In this pap…