1 citations · 2 across the 13 of their papers we have counts for
16 papers
Refine Connections, Close the Gap: A Reliable Enhancement Framework for Driving Scene Topology
Xiaoqi Wang, Dingyi Zhaung, Dingyi Zhuang +7
In autonomous driving, understanding scene topology - the connectivity between lanes and traffic elements - is critical for safe path planning and motion control. While current met…
Scaling Curriculum Learning For Autonomous Driving
Cevahir Koprulu, David Paz, Feng Tao +4
Batched simulators for autonomous driving have recently enabled training reinforcement learning (RL) agents at scale, encompassing thousands of traffic scenarios and billions of in…
Adapting Reinforcement Learning for Path Planning in Constrained Parking Scenarios
Feng Tao, Luca Paparusso, Chenyi Gu +6
Real-time path planning in constrained environments remains a fundamental challenge for autonomous systems. Traditional classical planners, while effective under perfect perception…
Dino-Diffusion Modular Designs Bridge the Cross-Domain Gap in Autonomous Parking
Zixuan Wu, Hengyuan Zhang, Ting-Hsuan Chen +4
Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather an…
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
Junjie Ye, David Paz, Hengyuan Zhang +5
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent ap…
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
Hengyuan Zhang, David Paz, Yuliang Guo +3
Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability. However, recent advancements often overlook cross-sens…
Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps
Hengyuan Zhang, David Paz, Yuliang Guo +5
Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when ge…
OSM vs HD Maps: Map Representations for Trajectory Prediction
Jing-Yan Liao, Parth Doshi, Zihan Zhang +2
While High Definition (HD) Maps have long been favored for their precise depictions of static road elements, their accessibility constraints and susceptibility to rapid environment…
Occlusion-Aware 2D and 3D Centerline Detection for Urban Driving via Automatic Label Generation
David Paz, Narayanan E. Ranganatha, Srinidhi K. Srinivas +2
This research work seeks to explore and identify strategies that can determine road topology information in 2D and 3D under highly dynamic urban driving scenarios. To facilitate th…
CLiNet: Joint Detection of Road Network Centerlines in 2D and 3D
David Paz, Srinidhi Kalgundi Srinivas, Yunchao Yao +1
This work introduces a new approach for joint detection of centerlines based on image data by localizing the features jointly in 2D and 3D. In contrast to existing work that focuse…
Robust Human Identity Anonymization using Pose Estimation
Hengyuan Zhang, Jing-Yan Liao, David Paz +1
Many outdoor autonomous mobile platforms require more human identity anonymized data to power their data-driven algorithms. The human identity anonymization should be robust so tha…
TridentNetV2: Lightweight Graphical Global Plan Representations for Dynamic Trajectory Generation
David Paz, Hao Xiang, Andrew Liang +1
We present a framework for dynamic trajectory generation for autonomous navigation, which does not rely on HD maps as the underlying representation. High Definition (HD) maps have…
TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation
David Paz, Hengyuan Zhang, Henrik I. Christensen
In recent years, various state of the art autonomous vehicle systems and architectures have been introduced. These methods include planners that depend on high-definition (HD) maps…
Auto-calibration Method Using Stop Signs for Urban Autonomous Driving Applications
Yunhai Han, Yuhan Liu, David Paz +1
Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to…
Probabilistic Semantic Mapping for Urban Autonomous Driving Applications
David Paz, Hengyuan Zhang, Qinru Li +2
Recent advancements in statistical learning and computational abilities have enabled autonomous vehicle technology to develop at a much faster rate. While many of the architectures…
Autonomous Vehicle Benchmarking using Unbiased Metrics
David Paz, Po-jung Lai, Nathan Chan +2
With the recent development of autonomous vehicle technology, there have been active efforts on the deployment of this technology at different scales that include urban and highway…