Ashmitha Jaysi Sivakumar

LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems

Aayush Agrawal, Ashmitha Jaysi Sivakumar, Ibrahim Kaif, Chayan Banerjee

Abstract Accurate lane segmentation is essential for effective path planning and lane following in autonomous driving, especially in scenarios with significant occlusion from vehicles and pedestrians. Existing models often struggle under such conditions, leading to unreliable navigation and safety risks. We propose …

in, au (code pays fourni par la source)

Automated Detection of Roadway and Traffic Control Conditions Using On-Board Image Processing

Ashmitha Jaysi Sivakumar, L Pragati, Lelitha Vanajakshi

Accurate detection of roadway and traffic control conditions is crucial for enhancing Advanced Driver Assistance Systems (ADAS) in dynamic environments. This study aims to leverage deep learning techniques, specifically YOLO series, for their identification in real-world scenarios. Detection of traffic signs, markings …

in (code pays fourni par la source)

Toll Plaza Performance Analytics Using Connected Vehicle Data

Ashmitha Jaysi Sivakumar, Rahul Suryakant Sakhare, Darcy Michael Bullock

in, us (code pays fourni par la source)

Impact of Privacy Filters and Fleet Changes on Connected Vehicle Trajectory Datasets for Intersection and Freeway Use Cases

Enrique D. Saldivar-Carranza, Rahul Suryakant Sakhare, Jairaj C. Desai, Jijo Kulathintekizhakethil Mathew et autres

Commercially available crowdsourced connected vehicle (CV) trajectory data have recently been used to provide stakeholders with actionable and scalable roadway mobility infrastructure performance measures. Transportation agencies and automotive original equipment manufacturers (OEMs) share a common vision of ensuring the privacy of motorists …

us (code pays fourni par la source)

LOID: Lane Occlusion Inpainting and Detection for Enhanced Autonomous Driving Systems

Aayush Agrawal, Ashmitha Jaysi Sivakumar, Ibrahim Kaif, Chayan Banerjee

Accurate lane detection is essential for effective path planning and lane following in autonomous driving, especially in scenarios with significant occlusion from vehicles and pedestrians. Existing models often struggle under such conditions, leading to unreliable navigation and safety risks. We propose two …