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 …
A recurrent neural network solution for predicting driver intention at unsignalized intersections
In this letter, we present a system capable of inferring intent from observed vehicles
traversing an unsignalized intersection, a task critical for the safe driving of autonomous …
traversing an unsignalized intersection, a task critical for the safe driving of autonomous …
End-to-end interpretable neural motion planner
In this paper, we propose a neural motion planner for learning to drive autonomously in
complex urban scenarios that include traffic-light handling, yielding, and interactions with …
complex urban scenarios that include traffic-light handling, yielding, and interactions with …
Pedestrian prediction by planning using deep neural networks
Accurate traffic participant prediction is the prerequisite for collision avoidance of
autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed …
autonomous vehicles. In this work, we propose to predict pedestrians using goal-directed …
Attention based vehicle trajectory prediction
K Messaoud, I Yahiaoui… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Self-driving vehicles need to continuously analyse the driving scene, understand the
behavior of other road users and predict their future trajectories in order to plan a safe …
behavior of other road users and predict their future trajectories in order to plan a safe …
[HTML][HTML] Pedestrian intention prediction: A convolutional bottom-up multi-task approach
The ability to predict pedestrian behaviour is crucial for road safety, traffic management
systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous …
systems, Advanced Driver Assistance Systems (ADAS), and more broadly autonomous …
Covernet: Multimodal behavior prediction using trajectory sets
T Phan-Minh, EC Grigore, FA Boulton… - Proceedings of the …, 2020 - openaccess.thecvf.com
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for
urban driving. Previous work has employed a variety of methods, including multimodal …
urban driving. Previous work has employed a variety of methods, including multimodal …
Long short term memory for driver intent prediction
Advanced Driver Assistance Systems have been shown to greatly improve road safety.
However, existing systems are typically reactive with an inability to understand complex …
However, existing systems are typically reactive with an inability to understand complex …
Naturalistic driver intention and path prediction using recurrent neural networks
Understanding the intentions of drivers at intersections is a critical component for
autonomous vehicles. Urban intersections that do not have traffic signals are a common …
autonomous vehicles. Urban intersections that do not have traffic signals are a common …
Learning to forecast pedestrian intention from pose dynamics
O Ghori, R Mackowiak, M Bautista… - 2018 IEEE Intelligent …, 2018 - ieeexplore.ieee.org
For an autonomous car, the ability to foresee a humans action is very useful for mitigating
the risk of a possible collision. To humans this pedestrian intention foresight comes naturally …
the risk of a possible collision. To humans this pedestrian intention foresight comes naturally …