Context‐aware trajectory prediction for autonomous driving in heterogeneous environments
The prediction of surrounding agent trajectories in heterogeneous traffic environments
remains a challenging task for autonomous driving due to several critical issues, such as …
remains a challenging task for autonomous driving due to several critical issues, such as …
Toward driver intention prediction for intelligent vehicles: A deep learning approach
MN Azadani, A Boukerche - 2021 IEEE 46th Conference on …, 2021 - ieeexplore.ieee.org
High-level scene understanding and situational awareness are fundamental for autonomous
vehicles before being widely used on public roads in a thoroughly efficient and safe manner …
vehicles before being widely used on public roads in a thoroughly efficient and safe manner …
Agen: Adaptable generative prediction networks for autonomous driving
In highly interactive driving scenarios, accurate prediction of other road participants is critical
for safe and efficient navigation of autonomous cars. Prediction is challenging due to the …
for safe and efficient navigation of autonomous cars. Prediction is challenging due to the …
End-to-end intersection handling using multi-agent deep reinforcement learning
AP Capasso, P Maramotti, A Dell'Eva… - 2021 IEEE Intelligent …, 2021 - ieeexplore.ieee.org
Navigating through intersections is one of the main challenging tasks for an autonomous
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …
vehicle. However, for the majority of intersections regulated by traffic lights, the problem …
Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the
perception, decision and control problems in an integrated way, which can be more …
perception, decision and control problems in an integrated way, which can be more …
Probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process
Predicting surrounding vehicle behaviors are critical to autonomous vehicles when
negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious …
negotiating in multi-vehicle interaction scenarios. Most existing approaches require tedious …
Graph-based spatial-temporal convolutional network for vehicle trajectory prediction in autonomous driving
Forecasting the trajectories of neighbor vehicles is a crucial step for decision making and
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
motion planning of autonomous vehicles. This paper proposes a graph-based spatial …
Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving
We address one of the crucial aspects necessary for safe and efficient operations of
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …
A multi-modal vehicle trajectory prediction framework via conditional diffusion model: A coarse-to-fine approach
Z Li, H Liang, H Wang, X Zheng, J Wang… - Knowledge-Based …, 2023 - Elsevier
Accurate prediction of the future motion of surrounding vehicles is crucial for ensuring the
safety of motion planning in autonomous vehicles. However, it is challenging to perform …
safety of motion planning in autonomous vehicles. However, it is challenging to perform …
Intention-aware long horizon trajectory prediction of surrounding vehicles using dual LSTM networks
As autonomous vehicles (AVs) need to interact with other road users, it is of importance to
comprehensively understand the dynamic traffic environment, especially the future possible …
comprehensively understand the dynamic traffic environment, especially the future possible …