Marc: Multipolicy and risk-aware contingency planning for autonomous driving
Generating safe and non-conservative behaviors in dense, dynamic environments remains
challenging for automated vehicles due to the stochastic nature of traffic participants' …
challenging for automated vehicles due to the stochastic nature of traffic participants' …
Safety-assured speculative planning with adaptive prediction
Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …
A safe hierarchical planning framework for complex driving scenarios based on reinforcement learning
Autonomous vehicles need to handle various traffic conditions and make safe and efficient
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
decisions and maneuvers. However, on the one hand, a single optimization/sampling-based …
Epsilon: An efficient planning system for automated vehicles in highly interactive environments
In this article, we present an efficient planning system for automated vehicles in highly
interactive environments (EPSILON). EPSILON is an efficient interaction-aware planning …
interactive environments (EPSILON). EPSILON is an efficient interaction-aware planning …
Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
M Klimke, B Völz, M Buchholz - 2023 IEEE Intelligent Vehicles …, 2023 - ieeexplore.ieee.org
Reinforcement learning has received high research interest for developing planning
approaches in automated driving. Most prior works consider the end-to-end planning task …
approaches in automated driving. Most prior works consider the end-to-end planning task …
Motion planner with fixed-horizon constrained reinforcement learning for complex autonomous driving scenarios
In autonomous driving, behavioral decision-making and trajectory planning remain huge
challenges due to the large amount of uncertainty in environments and complex interaction …
challenges due to the large amount of uncertainty in environments and complex interaction …
Game-theoretic planning for autonomous driving among risk-aware human drivers
We present a novel approach for risk-aware planning with human agents in multi-agent
traffic scenarios. Our approach takes into account the wide range of human driver behaviors …
traffic scenarios. Our approach takes into account the wide range of human driver behaviors …
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 …
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 …
Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving
Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …