Abstract
Nowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.
Highlights
IntroductionAutonomous driving plays a pivotal role to solve traffic and transportation problems in urban areas (traffic congestions, accidents, etc) and it is going to change the way of travelling in our world in the future [5]
In recent years, autonomous driving plays a pivotal role to solve traffic and transportation problems in urban areas and it is going to change the way of travelling in our world in the future [5]
An approach for autonomous driving navigation based on Deep Reinforcement Learning algorithms is shown, by using CARLA Simulator in order to both train and evaluate
Summary
Autonomous driving plays a pivotal role to solve traffic and transportation problems in urban areas (traffic congestions, accidents, etc) and it is going to change the way of travelling in our world in the future [5]. Most self-driving vehicles are geared up with multiple high-precision sensors such as LIDAR and cameras. Considering a typical AV architecture, the control layer consists of a set of processes that implements the vehicle control and navigation functionality. A well defined control layer makes the vehicle robust regardless the varying environment situations, such as the traffic participants, weather conditions or traffic scenario, on the premise of guarantying vehicle stability and covering the route provided by any global planner, assuming that the control layer is based on a previous mapping and path planning layer that loads the map and planes the route. A large number of classic controllers as [3, 30, 38] have been successfully implemented in AV architectures
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