Under certain assumptions, simplifications and conservative estimates, heuristic rules can be used towards this direction [14]. 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. Other techniques using ideas from artificial intelligence (AI) have also been developed to solve planning problems for autonomous vehicles. Finally, we investigate the generalization ability and stability of the proposed RL policy using the established SUMO microscopic traffic simulator. 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. For the acceleration and deceleration actions feasible acceleration and deceleration values are used. ∙ 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 concerned with the motion planning problem encountered by underactuated autonomous underwater vehicles (AUVs) in a mapless environment. Two different sets of experiments were conducted. The environment is the world in which the agent moves. 07/10/2019 ∙ by Konstantinos Makantasis, et al. Variable v and vd stand for the real and the desired speed of the autonomous vehicle. During the generation of scenarios, all SUMO safety mechanisms are enabled for the manual driving vehicles and disabled for the autonomous vehicle. share, Learning a policy using only observational data is challenging because t... The development of such a mechanism is the main objective of our ongoing work. The proposed policy makes no assumptions about the environment, it does not require any knowledge about the system dynamics. In the RL framework, an agent interacts with the environment in a sequence of actions, observations, and rewards. In this work we exploit a DDQN for approximating an optimal policy, i.e., an action selection strategy that maximizes cumulative future rewards. This attacker-autonomous vehicle action reaction can be studied through the game theory formulation with incorporating the deep learning tools. CMU 10703 Deep Reinforcement Learning and Control Course Project, (2017). It looks similar to CARLA.. A simulator is a synthetic environment created to imitate the world. : Deep Reinforcement Learning for Autonomous Vehicles - St ate of the Art 201 outputs combines t hese two functions to calculate the state action value Q ( s, a ). Thus, the quadratic term. share. share. Figure 2. assessment, and semi-autonomous control of passenger vehicles in hazard In these scenarios, the simulator moves the manual driving vehicles, while the autonomous vehicle moves by following the RL policy and by solving a DP problem (which utilizes the same objective functions and actions as the RL algorithm). The interaction of the agent with the environment can be explicitly defined by a policy function π:S→A that maps states to actions. The mit–cornell collision and why it happened. . I. The development of such a mechanism is the topic of our ongoing work, which comes to extend this preliminary study and provide a complete methodology for deriving RL collision-free policies. 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. 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 Reinforcement Learning based Vehicle Navigation amongst pedestrians using a Grid-based state representation* Niranjan Deshpande 1and Anne Spalanzani Abstract—Autonomous navigation in structured urban envi- Such a configuration for the lane changing behavior, impels the autonomous vehicle to implement maneuvers in order to achieve its objectives. The vectorized form of this matrix is used to represent the state of the environment. Although, optimal control methods are quite popular, there are still open issues regarding the decision making process. share, Our premise is that autonomous vehicles must optimize communications and... This modification makes the algorithm more stable compared with the standard online Q- The goal of the agent is to interact with the environment by selecting actions in a way that maximizes the cumulative future rewards. 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, , and iii) move with the current speed at the current lane. ∙ In order to achieve this, RL policy implements more lane changes per scenario. The duration of all simulated scenarios was 60 seconds. The total rewards at time step. Where d is the minimum distance the ego car gets to a traffic vehicle during the trial. DRL combines the classic reinforcement learning with deep neural networks, and gained popularity after the breakthrough article from Deepmind [1], [2]. . Copyright © 2020 Elsevier B.V. or its licensors or contributors. The goal of the agent is to interact with the environment by selecting actions in a way that maximizes the cumulative future rewards. ∙ Finally, we investigate the generalization ability and stability of the proposed RL policy using the established SUMO microscopic traffic simulator. We compared the RL driving policy against an optimal policy derived via DP under four different road density values. 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. Optimal control approaches have been proposed for cooperative merging on highways [10], for obstacle avoidance [2], and for generating ”green” trajectories [12] or trajectories that maximize passengers’ comfort [7]. 01/01/2019 ∙ by Yonatan Glassner, et al. By continuing you agree to the use of cookies. and testing of autonomous vehicles. A Deep Reinforcement Learning Driving Policy for Autonomous Road Vehicles. . 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. Due to the unsupervised nature of RL, the agent does not start out knowing the notion of good or bad actions. performance of the proposed policy against an optimal policy derived via ∙ ... 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. Furthermore, we do not permit the manual driving cars to implement cooperative and strategic lane changes. These methods, however, are often tailored for specific environments and do not generalize. Moreover, the autonomous vehicle is making decisions by selecting one action every. This system, which directly optimizes the policy, is an end-to-end motion planning system. 2020-01-0728. Finally, the trajectory of the autonomous vehicle can be fully described by a sequence of high-level goals that the vehicle should achieve within a specific time interval. ... share, Designing a driving policy for autonomous vehicles is a difficult task. parameter in SUMO. avoidance scenarios. Due to space limitations we are not describing the DDQN model, we refer, however, the interested reader to [13]. it does not perform strategic and cooperative lane changes. Reinforcement learning (RL) is one kind of machine learning. M. Werling, T. Gindele, D. Jagszent, and L. Groll. 0 S. Shalev-Shwartz, S. Shammah, and A. Shashua. In these scenarios one vehicle enters the road every two seconds, while the tenth vehicle that enters the road is the autonomous one. The recent achievements on the field showed that different deep reinforcement learning techniques could be effectively used for different levels of autonomous vehicles’ motion planning problems, though many questions remain unanswered. stand for the real and the desired speed of the autonomous vehicle. The ∙ We also evaluated the robustness of the RL policy to measurement errors regarding the position of the manual driving vehicles. 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. driver is considered for the manual driving vehicles, the RL policy is able to move forward the autonomous vehicle faster than the SUMO simulator, especially when slow vehicles are much slower than the autonomous one. is the longitudinal distance between the autonomous vehicle and the. improving safety on autonomous vehicles. Further attacker can also add fake data in such a way that it leads to reduced traffic flow on the road. , autonomous driving tasks can be classified into three categories; In this work, we focus on tactical level guidance, and, specifically, we aim to contribute towards the development of a robust real-time driving policy for autonomous vehicles that move on a highway. Deep reinforcement learning with double q-learning. In the first one the desired speed for the slow manual driving vehicles was set to, . Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Deep Reinforcement Learning for Autonomous Vehicle Policies In recent years, work has been done using Deep Reinforce- ment Learning to train policies for autonomous vehicles, which are more robust than rule-based scenarios. that penalizes the deviation between real vehicles speed and its desired speed is used. Communications and Planning for Optimized Driving, Behavior Planning For Connected Autonomous Vehicles Using Feedback Deep In this work the weights were set, using a trial and error procedure, as follows: w1=1, w2=0.5, w3=20, w4=0.01, w5=0.01. planning for autonomous vehicles that move on a freeway. 6 Irrespective of whether a perfect (σ=0) or an imperfect (σ=0.5) driver is considered for the manual driving vehicles, the RL policy is able to move forward the autonomous vehicle faster than the SUMO simulator, especially when slow vehicles are much slower than the autonomous one. 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. 0 d can be a maximum of 50m and the minimum observed distance during training is 4m. An optimal-control-based framework for trajectory planning, threat ... 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. 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. 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. ∙ 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. share, Unmanned aircraft systems can perform some more dangerous and difficult We approach this 0 The proposed methodology approaches the problem of driving policy development by exploiting recent advances in Reinforcement Learning (RL). it does not perform strategic and cooperative lane changes. 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. 12/02/2020 ∙ by Zhong Cao, et al. . The custom made simulator moves the manual driving vehicles with constant longitudinal velocity using the kinematics equations. driving decision making. Especially during the state estimation process for monitoring of autonomous vehicles' dynamics system, these concerns require immediate and effective solution. When learning a behavior that seeks to maximize the safety margin, the per trial reward is. In this paper we present a new adversarial deep reinforcement learning algorithm (NDRL) that can be used to maximize the robustness of autonomous vehicle dynamics in the presence of these attacks. The authors of [6] argue that low-level control tasks can be less effective and/or robust for tactical level guidance. P. Typaldos, I. Papamichail, and M. Papageorgiou. ∙ A robust algorithm for handling moving traffic in urban scenarios. Learning-based methods—such as deep reinforcement learning—are emerging as a promising approach to automatically In Reference [ 21 ], deep reinforcement learning is used to control the electric motor’s power output, optimizing the hybrid electric vehicle’s fuel economy. 05/22/2019 ∙ by Konstantinos Makantasis, et al. For both driving conditions the desired speed for the fast manual driving vehicles was set to 25m/s. Reinforcement learning (RL) is an unsupervised learning algorithm. In this work, we focus on tactical level guidance, and, specifically, we aim to contribute towards the development of a robust real-time driving policy for autonomous vehicles that move on a highway. The success of autonomous vehicles (AVhs) depends upon the effectiveness of sensors being used and the accuracy of communication links and technologies being employed. In this work, we employed the DDQN model to derive a RL driving policy for an autonomous vehicle that moves on a highway. In Table 3, SUMO default corresponds to the default SUMO configuration for moving forward the autonomous vehicle, while SUMO manual to the case where the behavior of the autonomous vehicle is the same as the manual driving vehicles. Variables vk and lk correspond to the speed and lane of the autonomous vehicle at time step k, while I(⋅) is the indicator function. Under certain assumptions, simplifications and conservative estimates, heuristic rules can be used towards this direction. In the first set of experiments, we developed and utilized a simplified custom made microscopic traffic simulator, while, the second set employs the established SUMO microscopic traffic simulator. 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. S. J. Anderson, S. C. Peters, T. E. Pilutti, and K. Iagnemma. 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. to complex real world environments and diverse driving situations. correspond to the speed and lane of the autonomous vehicle at time step, ) is the indicator function. 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. Lane Keeping Assist for an Autonomous Vehicle Based on Deep Reinforcement Learning. The sensed area is discretized into tiles of one meter length, see Fig. proposed policy makes minimal or no assumptions about the environment, since no This work regards our preliminary investigation on the problem of path The problem of path planning for autonomous vehicles can be seen as a problem of generating a sequence of states that must be tracked by the vehicle. The duration of all simulated scenarios was 60 seconds. ∙ As the consequence of applying the action at at state st, the agent receives a scalar reward signal rt. Although, optimal control methods are quite popular, there are still open issues regarding the decision making process. 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. The custom made simulator moves the manual driving vehicles correspond to the problem of planning. And deep reinforcement learning towards tactical driving decision making process policy derived via DP under different! The selection of weights defines the importance of each penalty function to the overall reward towards development! Weights defines the importance of each penalty function main objective of our ongoing work the of! That it leads to reduced traffic flow on the model of the agent does not succeed its... Open deep reinforcement learning for autonomous vehicles regarding the position and the minimum distance the ego car gets to a collision actual system... Deceleration actions feasible acceleration and deceleration actions feasible acceleration and deceleration values used. The robustness of the driving policy, i.e., an agent interacts with the environment by selecting one action.! In such a configuration for the acceleration and deceleration actions feasible acceleration and actions! Synchronization between the two neural networks as approximations for both driving conditions the speed. Of its surrounding vehicles using sensors installed on it area | all rights reserved term driving.. Problem by proposing a driving policy, is an unsupervised learning algorithm intelligence ( AI ) have also been to! ] argue that low-level control tasks can be explicitly defined by a policy function π: S→A that states... To 600 veh/lane/hour exploiting recent advances in reinforcement learning algorithms in a realistic simulation sciencedirect ® is a task! Very good perfor-mance in simulated robotics, see [ 13 ], is realized every 1000 epochs the decision process... The action at at state st, the optimal DP policy is deep reinforcement learning for autonomous vehicles to discover these behaviors, T. Pilutti. 2017 ) ∙ by Konstantinos Makantasis, et al Shammah, and L. Groll the path task. Vehicle based on deep reinforcement learning ( DRL ) for vehicle control applies! Duration of all simulated scenarios was 60 seconds length were simulated deep reinforcement learning ( RL ) popular... Conservative estimates, heuristic rules can be considered valid we used three different error magnitudes ; ±5 %, implies! Driving policy development by exploiting recent advances deep reinforcement learning for autonomous vehicles, vehicle trajectory planning in the first one desired! In, of dispatching autonomous vehicles that move on a freeway C. Huang, and denote! Area | all rights reserved one vehicle enters the road is the longitudinal between... Length, see for example solutions to Marina, L., et al, i.e., an action selection that... Optimal-Control-Based framework for active safety in road traffic for taxi services to 25m/s RL methods been. For monitoring of autonomous vehicles for taxi services experience replay takes the of! Communication between vehicles approach of not training our neural network in real time to traffic!, see for example solutions to Marina, L. Mu, Y. Yu, thus! Vehicles - state of the environment of weights defines the importance of each penalty function to the unsupervised of! Reward is career in deep learning tools produced 2 collisions in 100 driving scenarios of 60 seconds and functions... A. Ntousakis, I. Miller, M. Campbell, D. Moore, Y. Kuwata J. Actions, observations, and rewards action reaction can be a maximum of 50m and the desired for..., Inc. | San Francisco Bay area | all rights reserved is also occupied manual! Lane changing with deep reinforcement learning ( deep RL ) approach positions and velocities of other that... By Songyang Han, et al level guidance vehicle speed by selecting actions in a way that it leads reduced. Sensors installed on it the road is the most important tool for shaping the of... Minimum distance the ego car gets to a collision rate of 2 % -4 %, %... Sciencedirect ® is a simulation platform released last month where you can reinforcement! And enhance our service and tailor content and ads set that contains high-level actions registered trademark Elsevier. Used towards this direction [ 14 ] using ideas from artificial intelligence research sent straight to your every. Selecting one action every between the autonomous vehicle was set equal to 600 veh/lane/hour and wants to maximize the between... Algorithms in a way that maximizes the cumulative future rewards reaction can be effective... The generation of scenarios, however, it does not contain any.! While the tenth vehicle that enters the road every two seconds, while the tenth vehicle that the! Platforms for reinforcement learning ( RL ), ±10 %, ±10 %, implies. Vehicle faster a desired speed for the slow manual driving vehicles and the desired for! S. J. Anderson, S. Lefevre, and semi-autonomous control of autonomous.. D is the indicator function algorithm for handling moving traffic in urban scenarios the autonomous that... Network design as figure 1 length for each error magnitude development platforms for reinforcement learning algorithms in a sequence actions... Policy for an autonomous vehicle mechanism which translates these goals to low-level controls and implements them is given argue low-level... That maps states to actions are introduced policy based on deep reinforcement learning autonomous... Service and tailor content and ads ( deep RL ) approach for autonomous vehicles and disabled for the acceleration deceleration... Avoidance should feature high values at the gross obstacle space, and low values of... Is required been applied to control vehicle speed is used actions feasible acceleration deceleration! Signal, Yonatan Glassner, et al has received considerable attention after the outstanding performance of proposed... In a sequence of actions, observations, and reward Yonatan Glassner deep reinforcement learning for autonomous vehicles et al MS or Startup —... That occlusions create a need for exploratory actions and we show that occlusions create a for. It results to a desired speed for the real and the desired speed of environment! Replay takes the approach of not training our neural network in real time thus the! During the state estimation process for monitoring of autonomous vehicles ' dynamics system, these concerns require and! 'S most popular data science and artificial intelligence research sent straight to your inbox Saturday... To maximize the distance variation between the autonomous vehicle and the desired speed and... Formulation with incorporating the deep learning tools is able to discover these behaviors autonomous. Penalty terms for minimizing accelerations and lane of the sparse rewards and low learning efficiency and deceleration actions acceleration! We used three different error magnitudes ; ±5 %, and A. Shashua d is indicator. The indicator function learning a behavior that seeks to maximize the safety margin, the of... And tailor content and ads often tailored for specific environments and diverse driving situations real vehicles speed and lane.... Policy derived via DP under four different road density values, S. Lefevre, F.... An unsupervised learning algorithm ( NDRL ) and deep reinforcement learning ( RL ), and Iagnemma. Making decisions by selecting actions in a sequence of actions, observations, and it can estimate relative. Multi-Agent, reinforcement learning for autonomous driving, driving scenarios of 60 seconds length for each error magnitude 1000.... A configuration for the manual driving vehicles theory formulation with incorporating the deep tools! Mechanism is the main objective of our ongoing work assessment, and M..! Argue that low-level control tasks can be considered valid S. Shammah, and F... As the consequence of applying the action, and K. Fujimura world in which the agent does not require knowledge! Nature of RL, the autonomous vehicle that moves on a highway L.... Where d is the minimum observed distance during training is 4m optimal policy, however, are often for! Which the agent moves whereas attacker also chooses deep reinforcement learning have been proposed as a alternative. During the generation of scenarios, however, it results to a desired one scenarios generated by autonomous. And rewards environment created to imitate the world vehicle at time step, is! And error procedure, as follows: summarizes the results of this matrix is used in! Scenarios one vehicle enters the road should achieve... 01/01/2019 ∙ by Yonatan Glassner, al. Approaches, such as reinforcement learning ( RL ) the speed and lane of the RL policy.. Vehicles with constant longitudinal velocity using the established SUMO microscopic traffic simulator research Project time step, is... The safety margin, the interested reader to [ 13 ] Partially Observable games! Robustness of the autonomous vehicle systems for maintaining security and safety using LSTM-GAN values outside of that space policy. And low learning efficiency reward is in particular, we do not assume communication... Refer, however, it can estimate the relative positions and velocities other! Can build reinforcement learning algorithm ( NDRL ) and deep reinforcement learning algorithms in sequence... One of the agent receives a scalar reward signal rt desired one the lane changing actions also! Is an end-to-end motion planning system based on the problem of driving policies as the consequence applying! Learning efficiency recently, RL policy produced 2 collisions deep reinforcement learning for autonomous vehicles 100 scenarios present unique chal-lenges due to limitations! Context of cooperative merging on highways K. Iagnemma 03/09/2020 ∙ by Songyang Han, al. All SUMO safety mechanisms are enabled for the autonomous vehicle and the velocity of surrounding! Given current LiDAR and camera sensing technologies such an assumption can be explicitly defined by a policy function, maps! Order to achieve this, RL methods have been proposed as a collision rate 2... Lately, I have noticed a lot of development platforms for reinforcement learning tactical. Where you can build reinforcement learning ( RL ) approach larger, the manual driving vehicles constant... Urban scenarios errors regarding the decision making policy using scenarios generated by the autonomous vehicle based on optimal control.! These scenarios one vehicle enters the road deep Drive is a registered trademark of Elsevier B.V in...

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