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Recent advancements in end-to-end autonomous driving using deep learning: A survey
End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …
modular systems, such as their overwhelming complexity and propensity for error …
Self-driving cars: A survey
We survey research on self-driving cars published in the literature focusing on autonomous
cars developed since the DARPA challenges, which are equipped with an autonomy system …
cars developed since the DARPA challenges, which are equipped with an autonomy system …
Towards large-scale small object detection: Survey and benchmarks
With the rise of deep convolutional neural networks, object detection has achieved
prominent advances in past years. However, such prosperity could not camouflage the …
prominent advances in past years. However, such prosperity could not camouflage the …
CCTSDB 2021: a more comprehensive traffic sign detection benchmark
J Zhang, X Zou, LD Kuang, J Wang… - Human-centric …, 2022 - centaur.reading.ac.uk
Traffic signs are one of the most important information that guide cars to travel, and the
detection of traffic signs is an important component of autonomous driving and intelligent …
detection of traffic signs is an important component of autonomous driving and intelligent …
Deep learning for safe autonomous driving: Current challenges and future directions
Advances in information and signal processing technologies have a significant impact on
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
autonomous driving (AD), improving driving safety while minimizing the efforts of human …
Zenseact open dataset: A large-scale and diverse multimodal dataset for autonomous driving
Existing datasets for autonomous driving (AD) often lack diversity and long-range
capabilities, focusing instead on 360* perception and temporal reasoning. To address this …
capabilities, focusing instead on 360* perception and temporal reasoning. To address this …
Deep learning for large-scale traffic-sign detection and recognition
Automatic detection and recognition of traffic signs plays a crucial role in management of the
traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory …
traffic-sign inventory. It provides an accurate and timely way to manage traffic-sign inventory …
A survey of the four pillars for small object detection: Multiscale representation, contextual information, super-resolution, and region proposal
Although great progress has been made in generic object detection by advanced deep
learning techniques, detecting small objects from images is still a difficult and challenging …
learning techniques, detecting small objects from images is still a difficult and challenging …
Overview of environment perception for intelligent vehicles
This paper presents a comprehensive literature review on environment perception for
intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent …
intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent …
Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Real-time detection of traffic signs, the task of pinpointing a traffic sign's location in natural
images, is a challenging computer vision task of high industrial relevance. Various …
images, is a challenging computer vision task of high industrial relevance. Various …