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 …
Data-driven Traffic Simulation: A Comprehensive Review
Autonomous vehicles (AVs) have the potential to significantly revolutionize society by
providing a secure and efficient mode of transportation. Recent years have witnessed …
providing a secure and efficient mode of transportation. Recent years have witnessed …
Bat: Behavior-aware human-like trajectory prediction for autonomous driving
The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
overcome on the journey to fully autonomous vehicles. To address this challenge, we …
Robustness and adaptability of reinforcement learning-based cooperative autonomous driving in mixed-autonomy traffic
Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
the real world where they will be surrounded by human-driven vehicles (HVs) is extremely …
Proxemo: Gait-based emotion learning and multi-view proxemic fusion for socially-aware robot navigation
V Narayanan, BM Manoghar… - 2020 IEEE/RSJ …, 2020 - ieeexplore.ieee.org
We present ProxEmo, a novel end-to-end emotion prediction algorithm for socially aware
robot navigation among pedestrians. Our approach predicts the perceived emotions of a …
robot navigation among pedestrians. Our approach predicts the perceived emotions of a …
Gameplan: Game-theoretic multi-agent planning with human drivers at intersections, roundabouts, and merging
We present a new method for multi-agent planning involving human drivers and
autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging …
autonomous vehicles (AVs) in unsignaled intersections, roundabouts, and during merging …
Ego-vehicle action recognition based on semi-supervised contrastive learning
C Noguchi, T Tanizawa - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
In recent years, many automobiles have been equipped with cameras, which have
accumulated an enormous amount of video footage of driving scenes. Autonomous driving …
accumulated an enormous amount of video footage of driving scenes. Autonomous driving …
Multi-View Graph Convolution Network Reinforcement Learning for CAVs Cooperative Control in Highway Mixed Traffic
D Xu, P Liu, H Li, H Guo, Z Xie… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The control of connected autonomous vehicles (CAVs) for cooperative sensing and driving
in mixed traffic flows is critical for the development of intelligent transportation systems …
in mixed traffic flows is critical for the development of intelligent transportation systems …
D2-TPred: Discontinuous dependency for trajectory prediction under traffic lights
A profound understanding of inter-agent relationships and motion behaviors is important to
achieve high-quality planning when navigating in complex scenarios, especially at urban …
achieve high-quality planning when navigating in complex scenarios, especially at urban …
Using graph-theoretic machine learning to predict human driver behavior
Studies have shown that autonomous vehicles (AVs) behave conservatively in a traffic
environment composed of human drivers and do not adapt to local conditions and socio …
environment composed of human drivers and do not adapt to local conditions and socio …