31 citations · 36 across the 11 of their papers we have counts for
6 papers · 1 filter
Risk-Aware Lane Selection on Highway with Dynamic Obstacles
Sangjae Bae, David Isele, Kikuo Fujimura +1
This paper proposes a discretionary lane selection algorithm. In particular, highway driving is considered as a targeted scenario, where each lane has a different level of traffic…
Cooperation-Aware Lane Change Maneuver in Dense Traffic based on Model Predictive Control with Recurrent Neural Network
Sangjae Bae, Dhruv Saxena, Alireza Nakhaei +3
This paper presents a real-time lane change control framework of autonomous driving in dense traffic, which exploits cooperative behaviors of other drivers. This paper focuses on h…
Cooperation-Aware Reinforcement Learning for Merging in Dense Traffic
Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura +1
Decision making in dense traffic can be challenging for autonomous vehicles. An autonomous system only relying on predefined road priorities and considering other drivers as moving…
Reinforcement Learning with Probabilistic Guarantees for Autonomous Driving
Maxime Bouton, Jesper Karlsson, Alireza Nakhaei +3
Designing reliable decision strategies for autonomous urban driving is challenging. Reinforcement learning (RL) has been used to automatically derive suitable behavior in uncertain…
Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Maxime Bouton, Alireza Nakhaei, Kikuo Fujimura +1
Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participa…
Modeling Preemptive Behaviors for Uncommon Hazardous Situations From Demonstrations
Priyam Parashar, Akansel Cosgun, Alireza Nakhaei +1
This paper presents a learning from demonstration approach to programming safe, autonomous behaviors for uncommon driving scenarios. Simulation is used to re-create a targeted driv…