Soda10m: A large-scale 2d self/semi-supervised object detection dataset for autonomous driving
Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system,
we present a large-scale dataset for standardizing the evaluation of different self-supervised …
we present a large-scale dataset for standardizing the evaluation of different self-supervised …
End-to-end deep learning of lane detection and path prediction for real-time autonomous driving
DH Lee, JL Liu - Signal, Image and Video Processing, 2023 - Springer
Inspired by the UNet architecture of semantic image segmentation, we propose a lightweight
UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane …
UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane …
Computing systems for autonomous driving: State of the art and challenges
The recent proliferation of computing technologies (eg, sensors, computer vision, machine
learning, and hardware acceleration) and the broad deployment of communication …
learning, and hardware acceleration) and the broad deployment of communication …
Object recognition and detection with deep learning for autonomous driving applications
Autonomous driving requires reliable and accurate detection and recognition of surrounding
objects in real drivable environments. Although different object detection algorithms have …
objects in real drivable environments. Although different object detection algorithms have …
SINet: A scale-insensitive convolutional neural network for fast vehicle detection
Vision-based vehicle detection approaches achieve incredible success in recent years with
the development of deep convolutional neural network (CNN). However, existing CNN …
the development of deep convolutional neural network (CNN). However, existing CNN …
Traffic light recognition using deep learning and prior maps for autonomous cars
LC Possatti, R Guidolini, VB Cardoso… - … joint conference on …, 2019 - ieeexplore.ieee.org
Autonomous terrestrial vehicles must be capable of perceiving traffic lights and recognizing
their current states to share the streets with human drivers. Most of the time, human drivers …
their current states to share the streets with human drivers. Most of the time, human drivers …
Object detection learning techniques for autonomous vehicle applications
Autonomous vehicles have been considered as one of the most important trending topics in
the domain of intelligent transportation systems. It is expected that most of the leading …
the domain of intelligent transportation systems. It is expected that most of the leading …
A performance comparison of YOLOv8 models for traffic sign detection in the Robotaxi-full scale autonomous vehicle competition
The ability to recognize traffic signs is a critical skill for safe driving, as traffic signs provide
drivers with essential information about the road conditions, potential hazards, speed limits …
drivers with essential information about the road conditions, potential hazards, speed limits …
An Improved Deep Learning‐Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance
G Balan, S Arumugam, S Muthusamy… - … on Electrical Energy …, 2022 - Wiley Online Library
Technology for electric vehicles (EVs) is a developing subject that offers numerous
advantages, such as reduced operating costs. Since the goal of EVs has always been to …
advantages, such as reduced operating costs. Since the goal of EVs has always been to …
Squeezedet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving
Object detection is a crucial task for autonomous driving. In addition to requiring high
accuracy to ensure safety, object detection for autonomous driving also requires real-time …
accuracy to ensure safety, object detection for autonomous driving also requires real-time …