457 citations · 1.2k across the 55 of their papers we have counts for
17 papers · 1 filter
Streaming End-to-end Speech Recognition For Mobile Devices
Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar +17
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present nu…
Synthesis of Different Autonomous Vehicles Test Approaches
Zhiyuan Huang, Mansur Arief, Henry Lam +1
Currently, the most prevalent way to evaluate an autonomous vehicle is to directly test it on the public road. However, because of recent accidents caused by autonomous vehicles, i…
Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
Zuxin Liu, Mansur Arief, Ding Zhao
Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D views and precise distance measures under highly uncertain driving conditions. Its practical implementati…
A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters
Wenhao Ding, Wenshuo Wang, Ding Zhao
Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle tr…
An "Xcity" Optimization Approach to Designing Proving Grounds for Connected and Autonomous Vehicles
Rui Chen, Mansur Arief, Ding Zhao
Proving ground, or on-track testing has been an essential part of testing and validation process for connected and autonomous vehicles (CAV). Several world-class CAV proving ground…
Deep context: end-to-end contextual speech recognition
Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar +2
In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, w…
Understanding V2V Driving Scenarios through Traffic Primitives
Wenshuo Wang, Weiyang Zhang, Ding Zhao
Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's dec…
Clustering of Driving Encounter Scenarios Using Connected Vehicle Trajectories
Wenshuo Wang, Aditya Ramesh, Ding Zhao
Multi-vehicle interaction behavior classification and analysis offer in-depth knowledge to make an efficient decision for autonomous vehicles. This paper aims to cluster a wide ran…
Evaluation of the Energy Efficiency in a Mixed Traffic with Automated Vehicles and Human Controlled Vehicles
Xun Gong, Yaohui Guo, Yiheng Feng +2
The energy efficiency of Connected and Automated Vehicles (CAVs) is significantly influenced by surrounding road users. This paper presents the evaluation of energy efficiency of C…
An Optimal LiDAR Configuration Approach for Self-Driving Cars
Shenyu Mou, Yan Chang, Wenshuo Wang +1
LiDARs plays an important role in self-driving cars and its configuration such as the location placement for each LiDAR can influence object detection performance. This paper aims…
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives
Jiacheng Zhu, Wenshuo Wang, Ding Zhao
A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure an…
Energy Efficiency and Emission Testing for Connected and Automated Vehicles Using Real-World Driving Data
Yan Chang, Weiqing Yang, Ding Zhao
By using the onboard sensing and external connectivity technology, connected and automated vehicles (CAV) could lead to improved energy efficiency, better routing, and lower traffi…
An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets
Mansur Arief, Peter Glynn, Ding Zhao
Various automobile and mobility companies, for instance Ford, Uber and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads. However,…
Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks
Huajing Zhao, Zhaobin Mo, Macheng Shen +2
In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism mak…
Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning
Sisi Li, Wenshuo Wang, Zhaobin Mo +1
Learning knowledge from driving encounters could help self-driving cars make appropriate decisions when driving in complex settings with nearby vehicles engaged. This paper develop…
Extraction of V2V Encountering Scenarios from Naturalistic Driving Database
Zhaobin Mo, Sisi Li, Diange Yang +1
It is necessary to thoroughly evaluate the effectiveness and safety of Connected Vehicles (CVs) algorithm before their release and deployment. Current evaluation approach mainly re…
Learning and Inferring a Driver's Braking Action in Car-Following Scenarios
Wenshuo Wang, Junqiang Xi, Ding Zhao
Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's…