Explainable object-induced action decision for autonomous vehicles
A new paradigm is proposed for autonomous driving. The new paradigm lies between the
end-to-end and pipelined approaches, and is inspired by how humans solve the problem …
end-to-end and pipelined approaches, and is inspired by how humans solve the problem …
Introspection of dnn-based perception functions in automated driving systems: State-of-the-art and open research challenges
Automated driving systems (ADSs) aim to improve the safety, efficiency and comfort of future
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
vehicles. To achieve this, ADSs use sensors to collect raw data from their environment. This …
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 …
Agen: Adaptable generative prediction networks for autonomous driving
In highly interactive driving scenarios, accurate prediction of other road participants is critical
for safe and efficient navigation of autonomous cars. Prediction is challenging due to the …
for safe and efficient navigation of autonomous cars. Prediction is challenging due to the …
From spoken thoughts to automated driving commentary: Predicting and explaining intelligent vehicles' actions
D Omeiza, S Anjomshoae, H Webb… - 2022 IEEE Intelligent …, 2022 - ieeexplore.ieee.org
In commentary driving, drivers verbalise their observations, assessments and intentions. By
speaking out their thoughts, both learning and expert drivers are able to create a better …
speaking out their thoughts, both learning and expert drivers are able to create a better …
Automated evaluation of large vision-language models on self-driving corner cases
Large Vision-Language Models (LVLMs), due to the remarkable visual reasoning ability to
understand images and videos, have received widespread attention in the autonomous …
understand images and videos, have received widespread attention in the autonomous …
Explainable artificial intelligence (XAI): motivation, terminology, and taxonomy
A Notovich, H Chalutz-Ben Gal, I Ben-Gal - Machine Learning for Data …, 2023 - Springer
Deep learning algorithms and deep neural networks (DNNs) have become extremely
popular due to their high-performance accuracy in complex fields, such as image and text …
popular due to their high-performance accuracy in complex fields, such as image and text …
Failure prediction for autonomous driving
The primary focus of autonomous driving research is to improve driving accuracy. While
great progress has been made, state-of-the-art algorithms still fail at times. Such failures may …
great progress has been made, state-of-the-art algorithms still fail at times. Such failures may …
Hierarchical interpretable imitation learning for end-to-end autonomous driving
End-to-end autonomous driving provides a simple and efficient framework for autonomous
driving systems, which can directly obtain control commands from raw perception data …
driving systems, which can directly obtain control commands from raw perception data …
Neat: Neural attention fields for end-to-end autonomous driving
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …