A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …
experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The …
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 …
Extracting traffic primitives directly from naturalistically logged data for self-driving applications
Develo** an automated vehicle, that can handle complicated driving scenarios and
appropriately interact with other road users, requires the ability to semantically learn and …
appropriately interact with other road users, requires the ability to semantically learn and …
Driver-behavior modeling using on-road driving data: A new application for behavior signal processing
C Miyajima, K Takeda - IEEE Signal Processing Magazine, 2016 - ieeexplore.ieee.org
This article reviews data-centric approaches for statistical modeling of driver behavior.
Modeling driver behavior is challenging due to its stochastic nature and the high degree of …
Modeling driver behavior is challenging due to its stochastic nature and the high degree of …
Risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer
Advanced driver assistance and automated driving systems rely on risk estimation modules
to predict and avoid dangerous situations. Current methods use expensive sensor setups …
to predict and avoid dangerous situations. Current methods use expensive sensor setups …
Integrating driving behavior and traffic context through signal symbolization for data reduction and risky lane change detection
E Yurtsever, S Yamazaki, C Miyajima… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
A novel method for integrating driving behavior and traffic context through signal
symbolization is presented in this paper. This symbolization framework is proposed as a …
symbolization is presented in this paper. This symbolization framework is proposed as a …
Mobile computation in connected vehicles
In this chapter, we delve into the transformative role of connected vehicles as dynamic
computation platforms, transcending their conventional transportation functions. With the …
computation platforms, transcending their conventional transportation functions. With the …
A traffic flow simulation framework for learning driver heterogeneity from naturalistic driving data using autoencoders
This paper proposes a novel data-centric framework for microscopic traffic flow simulation
with intra and inter driver heterogeneity. We utilized a naturalistic driving corpus of 46 …
with intra and inter driver heterogeneity. We utilized a naturalistic driving corpus of 46 …
Navigating Risk: Deep Learning Approaches for Lane Change Safety and Lane Departure Warning Systems
In the realm of road safety, the advent of advanced driver assistance systems has propelled
the exploration of innovative technologies aimed at mitigating risks and preventing …
the exploration of innovative technologies aimed at mitigating risks and preventing …