380 citations · 1.9k across the 168 of their papers we have counts for
27 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…
COCOI: Contact-aware Online Context Inference for Generalizable Non-planar Pushing
Zhuo Xu, Wenhao Yu, Alexander Herzog +6
General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics. Deep reinforcement learning…
Learning Dense Rewards for Contact-Rich Manipulation Tasks
Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar +2
Rewards play a crucial role in reinforcement learning. To arrive at the desired policy, the design of a suitable reward function often requires significant domain expertise as well…
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…
Alternating Direction Method of Multipliers for Constrained Iterative LQR in Autonomous Driving
Jun Ma, Zilong Cheng, Xiaoxue Zhang +2
In the context of autonomous driving, the iterative linear quadratic regulator (iLQR) is known to be an efficient approach to deal with the nonlinear vehicle model in motion planni…
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…
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory
Ran Tian, Liting Sun, Masayoshi Tomizuka
Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumpti…
Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
Liting Sun, Zheng Wu, Hengbo Ma +1
In human-robot interaction (HRI) systems, such as autonomous vehicles, understanding and representing human behavior are important. Human behavior is naturally rich and diverse. Co…
Data-Driven Multi-Objective Controller Optimization for a Magnetically-Levitated Nanopositioning System
Xiaocong Li, Haiyue Zhu, Jun Ma +4
The performance achieved with traditional model-based control system design approaches typically relies heavily upon accurate modeling of the motion dynamics. However, modeling the…
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…
In Proximity of ReLU DNN, PWA Function, and Explicit MPC
Saman Fahandezh-Saadi, Masayoshi Tomizuka
Rectifier (ReLU) deep neural networks (DNN) and their connection with piecewise affine (PWA) functions is analyzed. The paper is an effort to find and study the possibility of repr…
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
Bichen Wu, Chenfeng Xu, Xiaoliang Dai +7
Computer vision has achieved remarkable success by (a) representing images as uniformly-arranged pixel arrays and (b) convolving highly-localized features. However, convolutions tr…
Guided Policy Search Model-based Reinforcement Learning for Urban Autonomous Driving
Zhuo Xu, Jianyu Chen, Masayoshi Tomizuka
In this paper, we continue our prior work on using imitation learning (IL) and model free reinforcement learning (RL) to learn driving policies for autonomous driving in urban scen…
Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks
Haonan Chang, Zhuo Xu, Masayoshi Tomizuka
Reinforcement learning methods have been developed to achieve great success in training control policies in various automation tasks. However, a main challenge of the wider applica…
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…
EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning
Jiachen Li, Fan Yang, Masayoshi Tomizuka +1
Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the s…
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning
Jianyu Chen, Zhuo Xu, Masayoshi Tomizuka
Current autonomous driving systems are composed of a perception system and a decision system. Both of them are divided into multiple subsystems built up with lots of human heuristi…
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…
Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network
Jiachen Li, Hengbo Ma, Zhihao Zhang +1
Effective understanding of the environment and accurate trajectory prediction of surrounding dynamic obstacles are indispensable for intelligent mobile systems (like autonomous veh…
Experimental Evaluation of Human Motion Prediction: Toward Safe and Efficient Human Robot Collaboration
Weiye Zhao, Liting Sun, Changliu Liu +1
Human motion prediction is non-trivial in modern industrial settings. Accurate prediction of human motion can not only improve efficiency in human robot collaboration, but also enh…