Search Results for author:
Found 7 papers, 4 papers with code
Towards Scalable & Efficient Interaction-Aware Planning in Autonomous Vehicles using Knowledge Distillation
Real-world driving involves intricate interactions among vehicles navigating through dense traffic scenarios.
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
This paper presents an online smooth-path lane-change control framework.
Active Uncertainty Reduction for Safe and Efficient Interaction Planning: A Shielding-Aware Dual Control Approach
To ensure the safe operation of the interacting agents, we use a runtime safety filter (also referred to as a "shielding" scheme), which overrides the robot's dual control policy with a safety fallback strategy when a safety-critical event is imminent.
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning
It can be difficult to autonomously produce driver behavior so that it appears natural to other traffic participants.
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
This paper proposes a discretionary lane selection algorithm.
Driving in Dense Traffic with Model-Free Reinforcement Learning
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers.
