Teaching Autonomous Vehicles to Express Interaction Intent during Unprotected Left Turns: A Human-Driving-Prior-Based Trajectory Planning Approach
With the integration of Autonomous Vehicles (AVs) into our transportation systems, their
harmonious coexistence with Human-driven Vehicles (HVs) in mixed traffic settings …
harmonious coexistence with Human-driven Vehicles (HVs) in mixed traffic settings …
Behavior and interaction-aware motion planning for autonomous driving vehicles based on hierarchical intention and motion prediction
Safe motion planning in complex and interactive environments is one of the major
challenges for developing autonomous vehicles. In this paper, we propose an interaction …
challenges for developing autonomous vehicles. In this paper, we propose an interaction …
Learning interaction-aware guidance policies for motion planning in dense traffic scenarios
B Brito, A Agarwal, J Alonso-Mora - arXiv preprint arXiv:2107.04538, 2021 - arxiv.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Learning interaction-aware guidance for trajectory optimization in dense traffic scenarios
B Brito, A Agarwal… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Autonomous navigation in dense traffic scenarios remains challenging for autonomous
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
vehicles (AVs) because the intentions of other drivers are not directly observable and AVs …
Bringing diversity to autonomous vehicles: An interpretable multi-vehicle decision-making and planning framework
With the development of autonomous driving, it is becoming increasingly common for
autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel on the same roads …
autonomous vehicles (AVs) and human-driven vehicles (HVs) to travel on the same roads …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …
Multimodal vehicular trajectory prediction with inverse reinforcement learning and risk aversion at urban unsignalized intersections
Understanding human drivers' intentions and predicting their future motions are significant to
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …
connected and autonomous vehicles and traffic safety and surveillance systems. Predicting …
Human-like highway trajectory modeling based on inverse reinforcement learning
Autonomous driving is one of the current cutting edge technologies. For autonomous cars,
their driving actions and trajectories should not only achieve autonomy and safety, but also …
their driving actions and trajectories should not only achieve autonomy and safety, but also …
[HTML][HTML] How do active road users act around autonomous vehicles? An inverse reinforcement learning approach
The inevitable impact of autonomous vehicles (AV) on traffic safety is becoming a reality with
the progressive deployment of these vehicles in different parts of the world. Still, many …
the progressive deployment of these vehicles in different parts of the world. Still, many …
Reinforcement learning based negotiation-aware motion planning of autonomous vehicles
Z Wang, Y Zhuang, Q Gu, D Chen… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
For autonomous vehicles integrating onto road-ways with human traffic participants, it
requires understanding and adapting to the participants' intention by responding in …
requires understanding and adapting to the participants' intention by responding in …
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