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 …
A survey on motion prediction and risk assessment for intelligent vehicles
With the objective to improve road safety, the automotive industry is moving toward more
“intelligent” vehicles. One of the major challenges is to detect dangerous situations and react …
“intelligent” vehicles. One of the major challenges is to detect dangerous situations and react …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
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 …
A survey on autonomous vehicle control in the era of mixed-autonomy: From physics-based to AI-guided driving policy learning
This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
methodologies from artificial intelligence (AI) into the field of transportation engineering for …
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 …
Modeling vehicle interactions via modified LSTM models for trajectory prediction
The long short-term memory (LSTM) model is one of the most commonly used vehicle
trajectory predicting models. In this paper, we study two problems of the existing LSTM …
trajectory predicting models. In this paper, we study two problems of the existing LSTM …
A data-driven lane-changing model based on deep learning
DF Xie, ZZ Fang, B Jia, Z He - Transportation research part C: emerging …, 2019 - Elsevier
Abstract Lane-changing (LC), which is one of the basic driving behavior, largely impacts on
traffic efficiency and safety. Modeling an LC process is challenging due to the complexity …
traffic efficiency and safety. Modeling an LC process is challenging due to the complexity …
Predicting the driver's focus of attention: the dr (eye) ve project
In this work we aim to predict the driver's focus of attention. The goal is to estimate what a
person would pay attention to while driving, and which part of the scene around the vehicle …
person would pay attention to while driving, and which part of the scene around the vehicle …
State estimation and motion prediction of vehicles and vulnerable road users for cooperative autonomous driving: A survey
The recent progress in autonomous vehicle research and development has led to
increasingly widespread testing of fully autonomous vehicles on public roads, where …
increasingly widespread testing of fully autonomous vehicles on public roads, where …