37 citations · 104 across the 11 of their papers we have counts for
12 papers
Traffic Control via Connected and Automated Vehicles: An Open-Road Field Experiment with 100 CAVs
Jonathan W. Lee, Han Wang, Kathy Jang +61
The CIRCLES project aims to reduce instabilities in traffic flow, which are naturally occurring phenomena due to human driving behavior. These "phantom jams" or "stop-and-go waves,…
Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results
Fang-Chieh Chou, Marsalis Gibson, Rahul Bhadani +2
Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm,…
Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers
Jonathan W. Lee, George Gunter, Rabie Ramadan +25
This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular atten…
Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Abhishek Mohta, Fang-Chieh Chou, Brian C. Becker +2
Detection of surrounding objects and their motion prediction are critical components of a self-driving system. Recently proposed models that jointly address these tasks rely on a n…
Uncertainty-Aware Vehicle Orientation Estimation for Joint Detection-Prediction Models
Henggang Cui, Fang-Chieh Chou, Jake Charland +2
Object detection is a critical component of a self-driving system, tasked with inferring the current states of the surrounding traffic actors. While there exist a number of studies…
Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
Sudeep Fadadu, Shreyash Pandey, Darshan Hegde +4
We present an end-to-end method for object detection and trajectory prediction utilizing multi-view representations of LiDAR returns and camera images. In this work, we recognize t…
MultiXNet: Multiclass Multistage Multimodal Motion Prediction
Nemanja Djuric, Henggang Cui, Zhaoen Su +13
One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the nea…
Improving Movement Predictions of Traffic Actors in Bird's-Eye View Models using GANs and Differentiable Trajectory Rasterization
Eason Wang, Henggang Cui, Sai Yalamanchi +3
One of the most critical pieces of the self-driving puzzle is the task of predicting future movement of surrounding traffic actors, which allows the autonomous vehicle to safely an…
Deep Kinematic Models for Kinematically Feasible Vehicle Trajectory Predictions
Henggang Cui, Thi Nguyen, Fang-Chieh Chou +4
Self-driving vehicles (SDVs) hold great potential for improving traffic safety and are poised to positively affect the quality of life of millions of people. To unlock this potenti…
Predicting Motion of Vulnerable Road Users using High-Definition Maps and Efficient ConvNets
Fang-Chieh Chou, Tsung-Han Lin, Henggang Cui +6
Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to…
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
Henggang Cui, Vladan Radosavljevic, Fang-Chieh Chou +5
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potentia…
Uncertainty-aware Short-term Motion Prediction of Traffic Actors for Autonomous Driving
Nemanja Djuric, Vladan Radosavljevic, Henggang Cui +5
We address one of the crucial aspects necessary for safe and efficient operations of autonomous vehicles, namely predicting future state of traffic actors in the autonomous vehicle…