A hierarchical behavior prediction framework at signalized intersections
Road user behavior prediction is one of the most critical components in trajectory planning
for autonomous driving, especially in urban scenarios involving traffic signals. In this paper …
for autonomous driving, especially in urban scenarios involving traffic signals. In this paper …
Recup net: Recursive prediction network for surrounding vehicle trajectory prediction with future trajectory feedback
In order to predict the behavior of human drivers accurately, the autonomous vehicle should
be able to understand the reasoning and decision process of motion generation of human …
be able to understand the reasoning and decision process of motion generation of human …
Trajectory planning with comfort and safety in dynamic traffic scenarios for autonomous driving
Trajectory planning is one of the most important modules of the Autonomous Driving
Systems (ADSs), which aims to achieve a safe and comfortable interaction between the …
Systems (ADSs), which aims to achieve a safe and comfortable interaction between the …
RACP: Risk-Aware Contingency Planning with Multi-Modal Predictions
For an autonomous vehicle to operate reliably within real-world traffic scenarios, it is
imperative to assess the repercussions of its prospective actions by anticipating the …
imperative to assess the repercussions of its prospective actions by anticipating the …
Learning-based MPC for Autonomous Motion Planning at Freeway Off-ramp Diverging
Off-ramp diverging road segment is the preparation area for vehicles driving away from the
freeway, while it causes more traffic conflicts making it a typical safety bottleneck. Focusing …
freeway, while it causes more traffic conflicts making it a typical safety bottleneck. Focusing …
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
This study introduces a novel approach to autonomous motion planning, informing an
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …
analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate …
Continuous decision making for on-road autonomous driving under uncertain and interactive environments
Although autonomous driving techniques have achieved great improvements, challenges
still exist in decision making for variety of different scenarios under uncertain and interactive …
still exist in decision making for variety of different scenarios under uncertain and interactive …
: Framework for Online Motion Planning Using Interaction-Aware Motion Predictions in Complex Driving Situations
Motion planning is a process of constant negotiation with the rest of the traffic agents and is
highly conditioned by their movement prediction. Indeed, an incorrect prediction could cause …
highly conditioned by their movement prediction. Indeed, an incorrect prediction could cause …
Integrating deep reinforcement learning with optimal trajectory planner for automated driving
Trajectory planning in the intersection is a challenging problem due to the strong uncertain
intentions of surrounding agents. Conventional methods may fail in some corner cases …
intentions of surrounding agents. Conventional methods may fail in some corner cases …
Learning interaction-aware motion prediction model for decision-making in autonomous driving
Predicting the behaviors of other road users is crucial to safe and intelligent decision-making
for autonomous vehicles (AVs). However, most motion prediction models ignore the …
for autonomous vehicles (AVs). However, most motion prediction models ignore the …