354 citations · 1.1k across the 115 of their papers we have counts for
10 papers · 1 filter
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
Di Feng, Zining Wang, Yiyang Zhou +5
The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity…
IDE-Net: Interactive Driving Event and Pattern Extraction from Human Data
Xiaosong Jia, Liting Sun, Masayoshi Tomizuka +1
Autonomous vehicles (AVs) need to share the road with multiple, heterogeneous road users in a variety of driving scenarios. It is overwhelming and unnecessary to carefully interact…
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
Peize Sun, Rufeng Zhang, Yi Jiang +8
We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as anchor boxes…
Prediction-Based Reachability for Collision Avoidance in Autonomous Driving
Anjian Li, Liting Sun, Wei Zhan +2
Safety is an important topic in autonomous driving since any collision may cause serious injury to people and damage to property. Hamilton-Jacobi (HJ) Reachability is a formal meth…
Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
Letian Wang, Liting Sun, Masayoshi Tomizuka +1
As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is becoming increasingly important. In order to gene…
Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning with Application to Autonomous Driving
Zheng Wu, Liting Sun, Wei Zhan +2
In the past decades, we have witnessed significant progress in the domain of autonomous driving. Advanced techniques based on optimization and reinforcement learning (RL) become in…
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
Hujie Pan, Zining Wang, Wei Zhan +1
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty fo…
Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction
Yeping Hu, Wei Zhan, Masayoshi Tomizuka
Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically c…
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Chenfeng Xu, Bichen Wu, Zining Wang +4
LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the \textit{de facto} method is to project a 3D point cloud…
Inferring Spatial Uncertainty in Object Detection
Zining Wang, Di Feng, Yiyang Zhou +5
The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-pro…