Zobrazeno 1 - 10
of 163
pro vyhledávání: '"Rangesh, P."'
Detecting road traffic signs and accurately determining how they can affect the driver's future actions is a critical task for safe autonomous driving systems. However, various traffic signs in a driving scene have an unequal impact on the driver's d
Externí odkaz:
http://arxiv.org/abs/2301.05804
To make safe transitions from autonomous to manual control, a vehicle must have a representation of the awareness of driver state; two metrics which quantify this state are the Observable Readiness Index and Takeover Time. In this work, we show that
Externí odkaz:
http://arxiv.org/abs/2301.05805
3-D object detection is pivotal for autonomous driving. Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information. NuTonomy's nuScenes dataset greatly extends commonly used datasets
Externí odkaz:
http://arxiv.org/abs/2205.12519
Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents. While all static scene elements are a source of information, there is asymmetric importance to the information
Externí odkaz:
http://arxiv.org/abs/2112.00942
Understanding occupant-vehicle interactions by modeling control transitions is important to ensure safe approaches to passenger vehicle automation. Models which contain contextual, semantically meaningful representations of driver states can be used
Externí odkaz:
http://arxiv.org/abs/2107.12932
With increasing automation in passenger vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key. In this study, we focus on the development of contextual, semantically meaningful representations of the drive
Externí odkaz:
http://arxiv.org/abs/2104.11489
This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields. These affinity fields, along with the binary masks, can then be used to cluster lane pixels horizontally and vertic
Externí odkaz:
http://arxiv.org/abs/2103.12040
Autor:
Rangesh, Akshay, Maheshwari, Pranav, Gebre, Mez, Mhatre, Siddhesh, Ramezani, Vahid, Trivedi, Mohan M.
This study follows many classical approaches to multi-object tracking (MOT) that model the problem using dynamic graphical data structures, and adapts this formulation to make it amenable to modern neural networks. Our main contributions in this work
Externí odkaz:
http://arxiv.org/abs/2101.04206
A driver's gaze is critical for determining their attention, state, situational awareness, and readiness to take over control from partially automated vehicles. Estimating the gaze direction is the most obvious way to gauge a driver's state under ide
Externí odkaz:
http://arxiv.org/abs/2002.02077
Autor:
Rangesh, Akshay, Trivedi, Mohan M.
This paper provides a simple solution for reliably solving image classification tasks tied to spatial locations of salient objects in the scene. Unlike conventional image classification approaches that are designed to be invariant to translations of
Externí odkaz:
http://arxiv.org/abs/1907.11824