Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
Human motion trajectory prediction: A survey
With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …
of such systems to perceive, understand, and anticipate human behavior becomes …
Spatio-temporal graph transformer networks for pedestrian trajectory prediction
Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …
systems and autonomous driving. This is challenging because it requires effectively …
Personalized vehicle trajectory prediction based on joint time-series modeling for connected vehicles
Motion prediction for the leading vehicle is a critical task for connected autonomous
vehicles. It provides a method to model the leading-following vehicle behavior and analysis …
vehicles. It provides a method to model the leading-following vehicle behavior and analysis …
Summit: A simulator for urban driving in massive mixed traffic
Autonomous driving in an unregulated urban crowd is an outstanding challenge, especially,
in the presence of many aggressive, high-speed traffic participants. This paper presents …
in the presence of many aggressive, high-speed traffic participants. This paper presents …
VR-ORCA: Variable responsibility optimal reciprocal collision avoidance
As one of the most popular multi-agent path planning approaches, the optimal reciprocal
collision avoidance (ORCA) algorithm assumes that each agent takes half the responsibility …
collision avoidance (ORCA) algorithm assumes that each agent takes half the responsibility …
[HTML][HTML] Conditional artificial potential field-based autonomous vehicle safety control with interference of lane changing in mixed traffic scenario
Car-following is an essential trajectory control strategy for the autonomous vehicle, which
not only improves traffic efficiency, but also reduces fuel consumption and emissions …
not only improves traffic efficiency, but also reduces fuel consumption and emissions …
Collision avoidance among dense heterogeneous agents using deep reinforcement learning
K Zhu, B Li, W Zhe, T Zhang - IEEE Robotics and Automation …, 2022 - ieeexplore.ieee.org
Navigating in a complex congested social environment without collision is a crucial and
challenging task. Recent studies have demonstrated the considerable success of Deep …
challenging task. Recent studies have demonstrated the considerable success of Deep …
Improving automated driving through POMDP planning with human internal states
Z Sunberg, MJ Kochenderfer - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
This work examines the hypothesis that partially observable Markov decision process
(POMDP) planning with human driver internal states can significantly improve both safety …
(POMDP) planning with human driver internal states can significantly improve both safety …
[HTML][HTML] 基于深度学习的行人轨迹预测方法综述
孔玮， 刘云， 李辉， 王传旭， 崔雪红 - 控制与决策, 2021 - kzyjc.alljournals.cn
为了规划合理的路径以规避行人, 针对行人轨迹预测的研究具有广泛的应用价值.
基于手工特征的传统方法难以预测复杂场景下的行人轨迹. 深度学习以人工神经网络为架构 …
基于手工特征的传统方法难以预测复杂场景下的行人轨迹. 深度学习以人工神经网络为架构 …