V
Vora Ankit Girish
Researcher at Ford Motor Company
Publications - 16
Citations - 297
Vora Ankit Girish is an academic researcher from Ford Motor Company. The author has contributed to research in topics: Executable & Computer science. The author has an hindex of 4, co-authored 16 publications receiving 101 citations.
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Journal ArticleDOI
Localization Requirements for Autonomous Vehicles.
Tyler G. R. Reid,Sarah E. Houts,Robert Cammarata,Graham Mills,Siddharth Agarwal,Vora Ankit Girish,Gaurav Pandey +6 more
TL;DR: In this article, the authors derived the safety integrity level, defining the allowable probability of failure per hour of operation based on desired improvements on road safety today, and then defined the geometry of the problem, where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on.
Journal ArticleDOI
Localization Requirements for Autonomous Vehicles
Tyler G. R. Reid,Sarah E. Houts,Robert Cammarata,Graham Mills,Siddharth Agarwal,Vora Ankit Girish,Gaurav Pandey +6 more
TL;DR: This work defines the geometry of the problem, where the aim is to maintain knowledge that the vehicle is within its lane and to determine what road level it is on, and derivesitudinal, lateral, and vertical localization error bounds and 95% accuracy requirements for autonomous vehicles based on first principles.
Journal ArticleDOI
Ford Multi-AV Seasonal Dataset:
Siddharth Agarwal,Vora Ankit Girish,Gaurav Pandey,Wayne Williams,Helen Kourous,James R. McBride +5 more
TL;DR: A challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles (AVs) at different days and times during 2017–2018 that can help design robust algorithms for AVs and multi- agent systems is presented.
Journal ArticleDOI
Ford Multi-AV Seasonal Dataset
Siddharth Agarwal,Vora Ankit Girish,Gaurav Pandey,Wayne Williams,Helen Kourous,James R. McBride +5 more
TL;DR: In this paper, the authors presented a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The dataset can help design robust algorithms for autonomous vehicles and multiagent systems.
Posted Content
Aerial Imagery based LIDAR Localization for Autonomous Vehicles.
TL;DR: This research concludes that aerial imagery based maps provides real-time localization performance similar to state-of-the-art LIDAR based maps for autonomous vehicles in urban environments at reduced costs.