[HTML][HTML] Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues
This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …
[HTML][HTML] Pedestrian and vehicle behaviour prediction in autonomous vehicle system—A review
LG Galvão, MN Huda - Expert Systems with Applications, 2024 - Elsevier
Autonomous vehicles (AV) s have become a trending topic nowadays since they have the
potential to solve traffic problems, such as accidents and congestion. Although AV systems …
potential to solve traffic problems, such as accidents and congestion. Although AV systems …
An LSTM network for highway trajectory prediction
F Altché, A de La Fortelle - 2017 IEEE 20th international …, 2017 - ieeexplore.ieee.org
In order to drive safely and efficiently on public roads, autonomous vehicles will have to
understand the intentions of surrounding vehicles, and adapt their own behavior …
understand the intentions of surrounding vehicles, and adapt their own behavior …
Tpnet: Trajectory proposal network for motion prediction
Making accurate motion prediction of the surrounding traffic agents such as pedestrians,
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
vehicles, and cyclists is crucial for autonomous driving. Recent data-driven motion prediction …
Dual transformer based prediction for lane change intentions and trajectories in mixed traffic environment
In a mixed traffic environment of human and autonomous driving, it is crucial for an
autonomous vehicle to predict the lane change intentions and trajectories of vehicles that …
autonomous vehicle to predict the lane change intentions and trajectories of vehicles that …
Probabilistic prediction of vehicle semantic intention and motion
Accurately predicting the possible behaviors of traffic participants is an essential capability
for future autonomous vehicles. The majority of current researches fix the number of driving …
for future autonomous vehicles. The majority of current researches fix the number of driving …
Modeling human driving behavior through generative adversarial imitation learning
An open problem in autonomous vehicle safety validation is building reliable models of
human driving behavior in simulation. This work presents an approach to learn neural …
human driving behavior in simulation. This work presents an approach to learn neural …
Environment-attention network for vehicle trajectory prediction
Y Cai, Z Wang, H Wang, L Chen, Y Li… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between
vehicles lies in constructing the interaction structure between the vehicles in the traffic …
vehicles lies in constructing the interaction structure between the vehicles in the traffic …
Non-local social pooling for vehicle trajectory prediction
K Messaoud, I Yahiaoui… - 2019 IEEE Intelligent …, 2019 - ieeexplore.ieee.org
For an efficient integration of autonomous vehicles on roads, human-like reasoning and
decision making in complex traffic situations are needed. One of the key factors to achieve …
decision making in complex traffic situations are needed. One of the key factors to achieve …
Convolution neural network-based lane change intention prediction of surrounding vehicles for ACC
Adaptive cruise control is one of the most widely used vehicle driver assistance systems.
However, uncertainty about drivers' lane change maneuvers in surrounding vehicles, such …
However, uncertainty about drivers' lane change maneuvers in surrounding vehicles, such …