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Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems
Autonomous vehicles rely on their perception systems to acquire information about their
immediate surroundings. It is necessary to detect the presence of other vehicles …
immediate surroundings. It is necessary to detect the presence of other vehicles …
Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
Behaviour prediction function of an autonomous vehicle predicts the future states of the
nearby vehicles based on the current and past observations of the surrounding environment …
nearby vehicles based on the current and past observations of the surrounding environment …
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 …
Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled intelligent transportation system
K Yu, L Lin, M Alazab, L Tan… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
It is expected that a mixture of autonomous and manual vehicles will persist as a part of the
intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues …
intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues …
Convolutional social pooling for vehicle trajectory prediction
Forecasting the motion of surrounding vehicles is a critical ability for an autonomous vehicle
deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic …
deployed in complex traffic. Motion of all vehicles in a scene is governed by the traffic …
Multi-modal trajectory prediction of surrounding vehicles with maneuver based lstms
To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles
need to have the ability to predict the future motion of surrounding vehicles. Multiple …
need to have the ability to predict the future motion of surrounding vehicles. Multiple …
An LSTM network for highway trajectory prediction
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 …
Intentnet: Learning to predict intention from raw sensor data
In order to plan a safe maneuver, self-driving vehicles need to understand the intent of other
traffic participants. We define intent as a combination of discrete high level behaviors as well …
traffic participants. We define intent as a combination of discrete high level behaviors as well …
Machine learning for autonomous vehicle's trajectory prediction: A comprehensive survey, challenges, and future research directions
The significant contribution of human errors, accounting for approximately 94%(with a
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
margin of±2.2%), to road crashes leading to casualties, vehicle damages, and safety …
Multimodal data capabilities for learning: What can multimodal data tell us about learning?
Most research on learning technology uses clickstreams and questionnaires as their primary
source of quantitative data. This study presents the outcomes of a systematic literature …
source of quantitative data. This study presents the outcomes of a systematic literature …