86 citations · 208 across the 12 of their papers we have counts for
13 papers
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
Sangjae Bae, David Isele, Alireza Nakhaei +5
This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drive…
Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
Maxime Bouton, Alireza Nakhaei, David Isele +2
Maneuvering in dense traffic is a challenging task for autonomous vehicles because it requires reasoning about the stochastic behaviors of many other participants. In addition, the…
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…
Safe Reinforcement Learning on Autonomous Vehicles
David Isele, Alireza Nakhaei, Kikuo Fujimura
There have been numerous advances in reinforcement learning, but the typically unconstrained exploration of the learning process prevents the adoption of these methods in many safe…
Driving in Dense Traffic with Model-Free Reinforcement Learning
Dhruv Mauria Saxena, Sangjae Bae, Alireza Nakhaei +2
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads. This is because the obstacle-…
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…
Interaction-aware Decision Making with Adaptive Strategies under Merging Scenarios
Yeping Hu, Alireza Nakhaei, Masayoshi Tomizuka +1
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road partic…
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
Jiachen Yang, Alireza Nakhaei, David Isele +2
A variety of cooperative multi-agent control problems require agents to achieve individual goals while contributing to collective success. This multi-goal multi-agent setting poses…
Collaborative Planning for Mixed-Autonomy Lane Merging
Shray Bansal, Akansel Cosgun, Alireza Nakhaei +1
Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Auton…
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…
Decomposition Methods with Deep Corrections for Reinforcement Learning
Maxime Bouton, Kyle Julian, Alireza Nakhaei +2
Decomposition methods have been proposed to approximate solutions to large sequential decision making problems. In contexts where an agent interacts with multiple entities, utility…