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Found 19 papers, 7 papers with code
Salient Sign Detection In Safe Autonomous Driving: AI Which Reasons Over Full Visual Context
Next, we use a custom salience loss function, Salience-Sensitive Focal Loss, to train a Deformable DETR object detection model in order to emphasize stronger performance on salient signs.
Safe Control Transitions: Machine Vision Based Observable Readiness Index and Data-Driven Takeover Time Prediction
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.
Structure Aware and Class Balanced 3D Object Detection on nuScenes Dataset
Point cloud based methods have become increasingly popular for 3-D object detection, owing to their accurate depth information.
On Salience-Sensitive Sign Classification in Autonomous Vehicle Path Planning: Experimental Explorations with a Novel Dataset
Safe path planning in autonomous driving is a complex task due to the interplay of static scene elements and uncertain surrounding agents.
Predicting Take-over Time for Autonomous Driving with Real-World Data: Robust Data Augmentation, Models, and Evaluation
Using the augmented dataset, we develop and train take-over time (TOT) models that operate sequentially on mid and high-level features produced by computer vision algorithms operating on different driver-facing camera views, showing models trained on the augmented dataset to outperform the initial dataset.
Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data
With increasing automation in passenger vehicles, the study of safe and smooth occupant-vehicle interaction and control transitions is key.
LaneAF: Robust Multi-Lane Detection with Affinity Fields
This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields.
Ranked #3 on
Lane Detection
on LLAMAS
TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking
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.
Gaze Preserving CycleGANs for Eyeglass Removal & Persistent Gaze Estimation
GPCycleGAN is based on the well-known CycleGAN approach - with the addition of a gaze classifier and a gaze consistency loss for additional supervision.
Forced Spatial Attention for Driver Foot Activity Classification
This paper provides a simple solution for reliably solving image classification tasks tied to spatial locations of salient objects in the scene.
3D BAT: A Semi-Automatic, Web-based 3D Annotation Toolbox for Full-Surround, Multi-Modal Data Streams
In this paper, we focus on obtaining 2D and 3D labels, as well as track IDs for objects on the road with the help of a novel 3D Bounding Box Annotation Toolbox (3D BAT).
Ground Plane Polling for 6DoF Pose Estimation of Objects on the Road
Once identified, the "best fit" plane provides enough constraints to successfully construct the desired 3D detection box, without directly predicting the 6DoF pose of the object.
HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver Hands
Tasks related to human hands have long been part of the computer vision community.
No Blind Spots: Full-Surround Multi-Object Tracking for Autonomous Vehicles using Cameras & LiDARs
In this paper, we present a modular framework for tracking multiple objects (vehicles), capable of accepting object proposals from different sensor modalities (vision and range) and a variable number of sensors, to produce continuous object tracks.
Driver Hand Localization and Grasp Analysis: A Vision-based Real-time Approach
This work addresses the task of accurately localizing driver hands and classifying the grasp state of each hand.
Driver Gaze Zone Estimation using Convolutional Neural Networks: A General Framework and Ablative Analysis
Finally, we evaluate our best performing model on the publicly available Columbia Gaze Dataset comprising of images from 56 subjects with varying head pose and gaze directions.
When Vehicles See Pedestrians with Phones:A Multi-Cue Framework for Recognizing Phone-based Activities of Pedestrians
The intelligent vehicle community has devoted considerable efforts to model driver behavior, and in particular to detect and overcome driver distraction in an effort to reduce accidents caused by driver negligence.
How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction
In this paper we propose a unified framework for surround vehicle maneuver classification and motion prediction that exploits multiple cues, namely, the estimated motion of vehicles, an understanding of typical motion patterns of freeway traffic and inter-vehicle interaction.
A Multimodal, Full-Surround Vehicular Testbed for Naturalistic Studies and Benchmarking: Design, Calibration and Deployment
Recent progress in autonomous and semi-autonomous driving has been made possible in part through an assortment of sensors that provide the intelligent agent with an enhanced perception of its surroundings.
