End-to-End automated lane-change maneuvering considering driving style using a deep deterministic policy gradient algorithm
Changing lanes while driving requires coordinating the lateral and longitudinal controls of a
vehicle, considering its running state and the surrounding environment. Although the …
vehicle, considering its running state and the surrounding environment. Although the …
Automated lane change strategy using proximal policy optimization-based deep reinforcement learning
Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan,
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane …
Integrating deep reinforcement learning with model-based path planners for automated driving
Automated driving in urban settings is challenging. Human participant behavior is difficult to
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
model, and conventional, rule-based Automated Driving Systems (ADSs) tend to fail when …
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
Driving safety is the most important element that needs to be considered for autonomous
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
Trajectory planning for autonomous vehicles using hierarchical reinforcement learning
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
driving problem significantly complex. Current heuristic-based algorithms such as the slot …
Trustworthy safety improvement for autonomous driving using reinforcement learning
Reinforcement learning (RL) can learn from past failures and has the potential to provide
self-improvement ability and higher-level intelligence. However, the current RL algorithms …
self-improvement ability and higher-level intelligence. However, the current RL algorithms …
Combining decision making and trajectory planning for lane changing using deep reinforcement learning
S Li, C Wei, Y Wang - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
In the context of Automated Vehicles, the Automated Lane Change system, is fundamentally
based upon the separate constructs of Perception, Decision making, Trajectory Planning …
based upon the separate constructs of Perception, Decision making, Trajectory Planning …
Deep distributional reinforcement learning based high-level driving policy determination
Even though some of the driver assistant systems have been commercialized to provide
safety and convenience to the driver, they can be applied for autonomous driving in limited …
safety and convenience to the driver, they can be applied for autonomous driving in limited …
Learning-based adaptive optimal control for connected vehicles in mixed traffic: Robustness to driver reaction time
Through vehicle-to-vehicle (V2V) communication, both human-driven and autonomous
vehicles can actively exchange data, such as velocities and bumper-to-bumper distances …
vehicles can actively exchange data, such as velocities and bumper-to-bumper distances …
Model-free deep reinforcement learning for urban autonomous driving
Urban autonomous driving decision making is challenging due to complex road geometry
and multi-agent interactions. Current decision making methods are mostly manually …
and multi-agent interactions. Current decision making methods are mostly manually …