Learning to predict vehicle trajectories with model-based planning
Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
paper, we introduce a novel prediction framework called PRIME, which stands for Prediction …
Cooperative merging control via trajectory optimization in mixed vehicular traffic
A major challenging issue related to the emerging mixed traffic vehicular system, composed
of connected and automated vehicles (CAVs) together with human-driven vehicles, is the …
of connected and automated vehicles (CAVs) together with human-driven vehicles, is the …
Towards a viable autonomous driving research platform
We present an autonomous driving research vehicle with minimal appearance modifications
that is capable of a wide range of autonomous and intelligent behaviors, including smooth …
that is capable of a wide range of autonomous and intelligent behaviors, including smooth …
Convolutional neural network-based lane-change strategy via motion image representation for automated and connected vehicles
The lane-change decision-making module of automated and connected vehicles (ACVs) is
one of the most crucial and challenging issues to be addressed. Motivated by human beings' …
one of the most crucial and challenging issues to be addressed. Motivated by human beings' …
Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms
Understanding and predicting highway lane-change maneuvers is essential for driving
modeling and its automation. The development of data-based lane-changing decision …
modeling and its automation. The development of data-based lane-changing decision …
A reinforcement learning approach to autonomous decision making of intelligent vehicles on highways
X Xu, L Zuo, X Li, L Qian, J Ren… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic
transportation environments. In this paper, a reinforcement learning approach with value …
transportation environments. In this paper, a reinforcement learning approach with value …
[图书][B] Handbook of driver assistance systems
H Winner, S Hakuli, F Lotz, C Singer - 2014 - researchgate.net
In this chapter, a survey of the current state of research on autonomous driving is given and
is set in the context of the requirements of an autonomous vehicle following the vision of an …
is set in the context of the requirements of an autonomous vehicle following the vision of an …
Stochastic model-predictive control for lane change decision of automated driving vehicles
This paper describes lane change motion planning with a combination of probabilistic and
deterministic prediction for automated driving under complex driving circumstances. The …
deterministic prediction for automated driving under complex driving circumstances. The …
Exploring spatial-temporal multi-frequency analysis for high-fidelity and temporal-consistency video prediction
B Jin, Y Hu, Q Tang, J Niu, Z Shi… - Proceedings of the …, 2020 - openaccess.thecvf.com
Video prediction is a pixel-wise dense prediction task to infer future frames based on past
frames. Missing appearance details and motion blur are still two major problems for current …
frames. Missing appearance details and motion blur are still two major problems for current …
Probabilistic online POMDP decision making for lane changes in fully automated driving
The Stadtpilot project aims at fully automated driving on Braunschweig's inner city ring road.
The TU Braunschweig's research vehicle “Leonie” is one of the first vehicles having the …
The TU Braunschweig's research vehicle “Leonie” is one of the first vehicles having the …