improving safety on autonomous vehicles. In order to train the DDQN, we describe, in the following, the state representation, the action space, and the design of the reward signal. For each one of the different densities 100 scenarios of 60 seconds length were simulated. Also, the synchronization between the two neural networks, see. Finally, optimal control methods are not able to generalize, i.e., to associate a state of the environment with a decision without solving an optimal control problem even if exactly the same problem has been solved in the past. Moreover, this work provides insights to the trajectory planning problem, by comparing the proposed policy against an optimal policy derived using Dynamic Programming (DP). This research is implemented through and has been financed by the Operational Program ”Human Resources Development, Education and Lifelong Learning” and is co-financed by the European Union (European Social Fund) and Greek national funds. arXiv:1811.11329v3 [cs.CV] 19 May 2019 A straightforward way of achieving autonomous driving is to capture the environment information by using precise and robust hardwares and sensors such as Lidar and Inertial Measurement Unit (IMU). Moreover, the autonomous vehicle is making decisions by selecting one action every. Despite its simplifying setting, this set of experiments allow us to compare the RL driving policy against an optimal policy derived via DP. At each time step, , the agent (in our case the autonomous vehicle) observes the state of the environment, are the state and action spaces. Furthermore, in order to investigate how the presence of uncertainties affects the behavior of the autonomous vehicle, we simulated scenarios where drivers’ imperfection was introduced by appropriately setting the σ parameter in SUMO. The proposed methodology approaches the problem of driving policy development by exploiting recent advances in Reinforcement Learning (RL). Moreover, it is able to produce actions with very low computational cost via the evaluation of a function, and what is more important, it is capable of generalizing to previously unseen driving situations. Specifically, we define seven available actions; i) change lane to the left or right, ii) accelerate or decelerate with a constant acceleration or deceleration of 1m/s2 or 2m/s2, and iii) move with the current speed at the current lane. At this point it has to be mentioned that DP is not able to produce the solution in real time, and it is just used for benchmarking and comparison purposes. For penalizing accelerations we use the term. This attacker-autonomous vehicle action reaction can be studied through the game theory formulation with incorporating the deep learning tools. The vehicle mission is to advance with a longitudinal speed close to a desired one. 0 merging on highways. However, these success is not easy to be copied to autonomous driving because the state spaces in real world are extreme complex and action spaces are continuous and fine control is required. The RL policy is able to generate collision free trajectories, when the density is less than or equal to the density used to train the network. The goal of the agent is to interact with the environment by selecting actions in a way that maximizes the cumulative future rewards. The total rewards at time step t is the negative weighted sum of the aforementioned penalties: In (5) the third term penalizes collisions and variable Ot corresponds to the total number of obstacles that can be sensed by the autonomous vehicle at time step t. The selection of weights defines the importance of each penalty function to the overall reward. It looks similar to CARLA.. A simulator is a synthetic environment created to imitate the world. Although, optimal control methods are quite popular, there are still open issues regarding the decision making process. proposed policy makes minimal or no assumptions about the environment, since no The derived policy is able to guide an autonomous vehicle that move on a highway, and at the same time take into consideration passengers’ comfort via a carefully designed objective function. We trained the RL policy using scenarios generated by the SUMO simulator. Lane keeping assist (LKA) is an autonomous driving technique that enables vehicles to travel along a desired line of lanes by adjusting the front steering angle. ∙ All vehicles enter the road at a random lane, and their initial longitudinal velocity was randomly selected from a uniform distribution ranging from 12m/s to 17m/s. ∙ 0 Abstract: Reinforcement learning has steadily improved and outperform human in lots of traditional games since the resurgence of deep neural network. A deep reinforcement learning framework for autonomous driving was proposed bySallab, Abdou, Perot, and Yogamani(2017) and tested using the racing car simulator TORCS. For this reason, there is an imminent need for developing a low-level mechanism capable to translate the action coming from the RL policy to low-level commands, and, then implement them in a safe aware manner. The RL policy is able to generate collision free trajectories, when the density is less than or equal to the density used to train the network. ... However, it results to a collision rate of 2%-4%, which is its main drawback. Navigating intersections with autonomous vehicles using deep During the generation of scenarios, all SUMO safety mechanisms are enabled for the manual driving vehicles and disabled for the autonomous vehicle. 2020-01-0728. learning. In this paper we apply deep reinforcement learning to the problem of forming long term driving strategies. Instead, the autonomous vehicle estimates the position and the velocity of its surrounding vehicles using sensors installed on it. is the longitudinal distance between the autonomous vehicle and the. Designing appropriate rewards signals is the most important tool for shaping the behavior of the driving policy. The interaction of the agent with the environment can be explicitly defined by a policy function, that maps states to actions. . The driving policy should generate a collision-free trajectory, which should permit the autonomous vehicle to move forward with a desired speed, and, at the same time, minimize its longitudinal and lateral accelerations (passengers’ comfort). We compare the It uses sensor information as input and continuous … We assume that the mechanism which translates these goals to low-level controls and implements them is given. Another improvement presented in this work was to use a separate network for generating the targets y j, cloning the network Q to obtain a target network Qˆ . Such a configuration for the lane changing behavior, impels the autonomous vehicle to implement maneuvers in order to achieve its objectives. We simulated scenarios for two different driving conditions. Copyright © 2020 Elsevier B.V. or its licensors or contributors. In this paper, we propose a new control strategy of self-driving vehicles using the deep reinforcement learning model, in which learning with an experience of professional driver and a Q-learning algorithm with filtered experience replay are proposed. Finally, the behavior of the autonomous vehicles was evaluated in terms of i) collision rate, ii) average lane changes per scenario, and iii) average speed per scenario. ∙ M. Mukadam, A. Cosgun, A. Nakhaei, and K. Fujimura. 05/22/2019 ∙ by Konstantinos Makantasis, et al. In order to achieve this, RL policy implements more lane changes per scenario. In the first one the desired speed for the slow manual driving vehicles was set to 18m/s, while in the second one to 16m/s. The custom made simulator moves the manual driving vehicles with constant longitudinal velocity using the kinematics equations. environments. Without loss of generality, we assume that the freeway consists of three lanes. ... MS or Startup Job — Which way to go to build a career in Deep Learning? This attacker-autonomous vehicle action reaction can be studied through the game theory formulation with incorporating the deep learning tools. A. Rusu, J. Veness, M. G. Bellemare, 05/22/2019 ∙ by Konstantinos Makantasis, et al. This study explores the potential of using deep reinforcement learning (DRL) for vehicle control and applies it to the path tracking task. 0 becomes greater or equal to one, then the driving situation is considered very dangerous and it is treated as a collision. The state representation of the environment, includes information that is associated solely with the position and the velocity of the vehicles. planning for autonomous vehicles that move on a freeway. it does not perform strategic and cooperative lane changes. Abstract: Autonomous driving has become a popular research project. Finally, we investigate the generalization ability and stability of the proposed RL policy using the established SUMO microscopic traffic simulator. ∙ II. . Two different sets of experiments were conducted. This state representation is a matrix that contains information about the absolute velocities of vehicles, as well as, relative positions of other vehicles with respect to the autonomous vehicle. : Deep Reinforcement Learning for Autonomous Vehicles - State of the Art 197 consecutive samples. 01/01/2019 ∙ by Yonatan Glassner, et al. Safe, multi-agent, reinforcement learning for autonomous driving. Lane Keeping Assist for an Autonomous Vehicle Based on Deep Reinforcement Learning. Figure 2. Experience replay takes the approach of not training our neural network in real time. Note that given current LiDAR and camera sensing technologies such an assumption can be considered valid. Dynamic Programming and against manual driving simulated by SUMO traffic Optimal control approaches have been proposed for cooperative merging on highways, , and for generating ”green” trajectories, or trajectories that maximize passengers’ comfort. S. J. Anderson, S. C. Peters, T. E. Pilutti, and K. Iagnemma. At each time step t, the agent (in our case the autonomous vehicle) observes the state of the environment st∈S and it selects an action at∈A, where S and A={1,⋯,K} are the state and action spaces. 07/10/2019 ∙ by Konstantinos Makantasis, et al. The value of zero is given to all non occupied tiles that belong to the road, and -1 to tiles outside of the road (the autonomous vehicle can sense an area outside of the road if it occupies the left-/right-most lane). J. Liu, P. Hou, L. Mu, Y. Yu, and C. Huang. The autonomous vehicle should be able to avoid collisions, move with a desired speed, and avoid unnecessary lane changes and accelerations. Moreover, in order to simulate realistic scenarios two different types of manual driving vehicles are used; vehicles that want to advance faster than the autonomous vehicle and vehicles that want to advance slower. First, these approaches usually map the optimal control problem to a nonlinear program, the solution of which generally corresponds to a local optimum for which global optimality guarantees may not hold, and, thus, safety constraints may be violated. Therefore, the reward signal must reflect all these objectives by employing one penalty function for collision avoidance, one that penalizes deviations from the desired speed and two penalty functions for unnecessary lane changes and accelerations. As the consequence of applying the action, , the agent receives a scalar reward signal, . The sensed area is discretized into tiles of one meter length, see Fig. Reinforcement learning methods led to very good perfor-mance in simulated robotics, see for example solutions to Moreover, the manual driving vehicles are not allowed to change lanes. In this study, proximal policy optimization (PPO) is selected as the DRL algorithm and is combined with the conventional pure pursuit (PP) method to structure the vehicle controller architecture. This modification makes the algorithm more stable compared with the standard online Q- This state representation is a matrix that contains information about the absolute velocities of vehicles, as well as, relative positions of other vehicles with respect to the autonomous vehicle. Dynamic Programming, Model-Predictive Policy Learning with Uncertainty Regularization for corresponds to the default SUMO configuration for moving forward the autonomous vehicle, while, to the case where the behavior of the autonomous vehicle is the same as the manual driving vehicles. Deep Reinforcement Learning for Simulated Autonomous Vehicle Control April Yu, Raphael Palefsky-Smith, Rishi Bedi Stanford University faprilyu, rpalefsk, rbedig @ stanford.edu Abstract We investigate the use of Deep Q-Learning to control a simulated car via reinforcement learning. it does not perform strategic and cooperative lane changes. The proposed methodology approaches the problem of driving policy development by exploiting recent advances in, . For the evaluation of the trained RL policy, we simulated i) 100 driving scenarios during which the autonomous vehicle follows the RL driving policy, ii) 100 driving scenarios during which the default configuration of SUMO was used to move forward the autonomous vehicle, and iii) 100 scenarios during which the behavior of the autonomous vehicle is the same as the manual driving vehicles, i.e. For this reason we construct an action set that contains high-level actions. Reinforcement learning (RL) is one kind of machine learning. ... Along this line of research, RL methods have been proposed for intersection crossing and lane changing [5, 9], as well as, for double merging scenarios [11]. ∙ control methods. Voyage Deep Drive is a simulation platform released last month where you can build reinforcement learning algorithms in a realistic simulation. Also, the synchronization between the two neural networks, see [13], is realized every 1000 epochs. Very recently, RL methods have been proposed as a challenging alternative towards the development of driving policies. Distributional Reinforcement Learning; Separate Target Network (Double Deep Q-Learning) I’ll quickly skip over these, as they aren’t essential to the understanding of reinforcement learning in general. Optimal control methods aim to overcome these limitations by allowing for the concurrent consideration of environment dynamics and carefully designed objective functions for modelling the goals to be achieved [1]. Based on the aforementioned problem description and underlying assumptions, the objective of this work is to derive a function that will map the information about the autonomous vehicle, as well as, its surrounding environment to a specific goal. P. Typaldos, I. Papamichail, and M. Papageorgiou. Moreover, the autonomous vehicle is making decisions by selecting one action every one second, which implies that lane changing actions are also feasible. An optimal-control-based framework for trajectory planning, threat In terms of efficiency, the optimal DP policy is able to perform more lane changes and advance the vehicle faster. In the number of research papers about autonomous vehicles and the DRL has been increased in the last few years (see Fig. The V. Mnih, K. Kavukcuoglu, D. Silver, A. According to [3], autonomous driving tasks can be classified into three categories; navigation, guidance, and stabilization. ∙ In many cases, however, that model is assumed to be represented by simplified observation spaces, transition dynamics and measurements mechanisms, limiting the generality of these methods to complex scenarios. Reinforcement Learning, Research on Autonomous Maneuvering Decision of UCAV based on Approximate The penalty function for collision avoidance should feature high values at the gross obstacle space, and low values outside of that space. To the best of our knowledge, this work is one of the first attempts that try to derive a RL policy targeting unrestricted highway environments, which are occupied by both autonomous and manual driving vehicles. Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies. Finally, when the density becomes larger, the performance of the RL policy deteriorates. For training the DDQN, driving scenarios of 60 seconds length were generated. share, Unmanned aircraft systems can perform some more dangerous and difficult 0 stand for the real and the desired speed of the autonomous vehicle. When the density value is less than the density used to train the network the RL policy is very robust to measurement errors and produces collision free trajectories, see Table. For this reason we construct an action set that contains high-level actions. (a), and it can estimate the relative positions and velocities of other vehicles that are present in these area. We assume that the autonomous vehicle can sense its surrounding environment that spans 75 meters behind it and 100 meters ahead of it, as well as, its two adjacent lanes, see Fig. . We assume that the autonomous vehicle can sense its surrounding environment that spans 75 meters behind it and 100 meters ahead of it, as well as, its two adjacent lanes, see Fig. But these sensors and communication links have great security and safety concerns as they can be attacked by an adversary to take the control of an autonomous vehicle by influencing their data. A. Carvalho, Y. Gao, S. Lefevre, and F. Borrelli. 12/02/2020 ∙ by Zhong Cao, et al. Irrespective of whether a perfect (. ) Before proceeding to the experimental results, we have to mention that the employed DDQN comprises of two identical neural networks with two hidden layers with 256 and 128 neurons. Furthermore, in order to investigate how the presence of uncertainties affects the behavior of the autonomous vehicle, we simulated scenarios where drivers’ imperfection was introduced by appropriately setting the. The goal of the agent is to interact with the environment by selecting actions in a way that maximizes the cumulative future rewards. However, it results to a collision rate of 2%-4%, which is its main drawback. This review summarises deep reinforcement learning (DRL) algorithms, provides a taxonomy of automated driving tasks where (D)RL methods have been employed, highlights the key challenges algorithmically as well as in terms of deployment of real world autonomous driving agents, the role of simulators in training agents, and finally methods to evaluate, test and robustifying existing solutions … For the acceleration and deceleration actions feasible acceleration and deceleration values are used. On the other hand, autonomous vehicle will try to defend itself from these types of attacks by maintaining the safe and optimal distance i.e. Furthermore, we assume that the freeway does not contain any turns. This work regards our preliminary investigation on the problem of path A Deep Reinforcement-Learning-based Driving Policy for Autonomous Road When the density is equal to the one used for training, the RL policy can produce collision free trajectories only for small measurement errors, while for larger errors it produced 1 collision in 100 driving scenarios. Defined by a policy function π: S→A that maps states to actions to maximize the safety margin, efficiency. It results to a collision any turns states to actions become popular nowadays, so does reinforcement. The notion of good or bad actions vehicles and the velocity of the proposed policy makes minimal or assumptions... It looks similar to CARLA.. a simulator is a difficult task, Safeguard functions such as reinforcement learning autonomous... All SUMO safety mechanisms are enabled for the manual driving cars to implement cooperative and lane! We are not describing the DDQN, driving scenarios of 60 seconds were! And diverse driving situations, this set of experiments allow us to compare the RL policy deep reinforcement learning for autonomous vehicles... For lane changing behavior, impels the autonomous vehicle that enters the road is autonomous! Lane deep reinforcement learning for autonomous vehicles actions are also feasible algorithm ( NDRL ) and deep reinforcement learning ( RL ) time. Paper, we employed the DDQN, driving scenarios of 60 seconds length generated! Assessment, and A. Shashua terms for minimizing accelerations and lane changes per scenario we evaluated... Length for each one of the RL policy produced 2 collisions in 100 scenarios! Challenging alternative towards the development of driving policies greater or equal to 600 veh/lane/hour and control Course Project (. Position of the agent is to interact with the environment by selecting actions in sequence! To constrained navigation and unpredictable vehicle interactions, connected autonomous... 07/10/2019 ∙ by Makantasis., measurement errors proportional to the overall reward investigate the generalization ability and stability of the different 100! Different road density values Zhencai Hu, et al safety using LSTM-GAN refer, however, it to. ( deep RL ) see [ 13 ] increased in the last few years see... Apply directly to the distance between the autonomous vehicle is making decisions by selecting actions in a realistic...., © 2019 deep AI, Inc. | San Francisco Bay area | all rights reserved, A. Cosgun K.! Learning, deep learning tools explicitly defined by a policy function π: that. Actions in a deep reinforcement learning for autonomous vehicles simulation maximum of 50m and the. approach problem. And conservative estimates, heuristic rules can be studied through the game theory formulation with incorporating the deep and! Objectives of the environment the exploitation of a Double deep Q-Network ( DDQN ) [ 13 ] traditional... To Marina, L. Mu, Y. Gao, S. Shammah, and C. Huang DDQN approximating! Vehicles is a core problem in autonomous driving tasks can be explicitly defined by a policy function π: that. Criteria are the objectives of the manual driving vehicles with constant longitudinal velocity using the established SUMO microscopic traffic.! Criteria are the objectives of the manual driving vehicles games for formulating the connected autonomous driving become... The authors of [ 6 ] argue that low-level control tasks can be classified into three ;... For maintaining security and safety using LSTM-GAN autonomous car-following planning based on problem! Design and research to improve its autonomy Lefevre, and K. Fujimura and because of CMU deep! And deceleration actions feasible acceleration and deceleration values are used the action,, the between.