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Found 30 papers, 9 papers with code
ActiveAnno3D - An Active Learning Framework for Multi-Modal 3D Object Detection
We propose ActiveAnno3D, an active learning framework to select data samples for labeling that are of maximum informativeness for training.
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration
Active learning strategies for 3D object detection in autonomous driving datasets may help to address challenges of data imbalance, redundancy, and high-dimensional data.
Vision-based Analysis of Driver Activity and Driving Performance Under the Influence of Alcohol
About 30% of all traffic crash fatalities in the United States involve drunk drivers, making the prevention of drunk driving paramount to vehicle safety in the US and other locations which have a high prevalence of driving while under the influence of alcohol.
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.
Trajectory Prediction for Autonomous Driving based on Multi-Head Attention with Joint Agent-Map Representation
The future trajectories of agents can be inferred using two important cues: the locations and past motion of agents, and the static scene structure.
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.
Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans
We address the problem of forecasting pedestrian and vehicle trajectories in unknown environments, conditioned on their past motion and scene structure.
Ranked #10 on
Trajectory Prediction
on Stanford Drone
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.
Scene Induced Multi-Modal Trajectory Forecasting via Planning
We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem.
Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis
In this paper, we present a data-driven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior.
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.
Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness
Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles.
Convolutional Social Pooling for Vehicle Trajectory Prediction
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle deployed in complex traffic.
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs
To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles.
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.
Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields
In the context of autonomous driving, where humans may need to take over in the event where the computer may issue a takeover request, a key step towards driving safety is the monitoring of the hands to ensure the driver is ready for such a request.
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.
An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation
A key step to driver safety is to observe the driver's activities with the face being a key step in this process to extracting information such as head pose, blink rate, yawns, talking to passenger which can then help derive higher level information such as distraction, drowsiness, intent, and where they are looking.
Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction
The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode.
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.
To Boost or Not to Boost? On the Limits of Boosted Trees for Object Detection
We aim to study the modeling limitations of the commonly employed boosted decision trees classifier.
Ranked #33 on
Face Detection
on WIDER Face (Medium)
Learning to Detect Vehicles by Clustering Appearance Patterns
This paper studies efficient means for dealing with intra-category diversity in object detection.
