Social interactions for autonomous driving: A review and perspectives
No human drives a car in a vacuum; she/he must negotiate with other road users to achieve
their goals in social traffic scenes. A rational human driver can interact with other road users …
their goals in social traffic scenes. A rational human driver can interact with other road users …
Detecting human driver inattentive and aggressive driving behavior using deep learning: Recent advances, requirements and open challenges
Human drivers have different driving styles, experiences, and emotions due to unique
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
driving characteristics, exhibiting their own driving behaviors and habits. Various research …
Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …
recent years related to several topics as sensor technologies, V2X communications, safety …
A survey of deep RL and IL for autonomous driving policy learning
Z Zhu, H Zhao - IEEE Transactions on Intelligent Transportation …, 2021 - ieeexplore.ieee.org
Autonomous driving (AD) agents generate driving policies based on online perception
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
results, which are obtained at multiple levels of abstraction, eg, behavior planning, motion …
Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic
On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
traffic where AVs coexist with human-driven vehicles (HDVs). In this paper, we formulate the …
Robust decision making for autonomous vehicles at highway on-ramps: A constrained adversarial reinforcement learning approach
Reinforcement learning has demonstrated its potential in a series of challenging domains.
However, many real-world decision making tasks involve unpredictable environmental …
However, many real-world decision making tasks involve unpredictable environmental …
Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios
Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …
especially in dense traffic, because the merging vehicle typically needs to interact with other …
Novel decision-making strategy for connected and autonomous vehicles in highway on-ramp merging
Z el abidine Kherroubi, S Aknine… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
High-speed highway on-ramp merging is a significant challenge toward realizing fully
automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine …
automated driving (level 4). Connected Autonomous Vehicles (CAVs), that combine …
Reinforcement learning-based autonomous driving at intersections in CARLA simulator
Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …
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. This paper focuses on …
dense Traffic, which exploits cooperative behaviors of other drivers. This paper focuses on …