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End-to-end Autonomous Driving in Heterogeneous Traffic Scenario Using Deep Reinforcement Learning | IEEE Conference Publication | IEEE Xplore

End-to-end Autonomous Driving in Heterogeneous Traffic Scenario Using Deep Reinforcement Learning


Abstract:

In this paper, we propose an end-to-end autonomous driving architecture for safe maneuvering in heterogeneous traffic using a reinforcement learning (RL) algorithm. Using...Show More

Abstract:

In this paper, we propose an end-to-end autonomous driving architecture for safe maneuvering in heterogeneous traffic using a reinforcement learning (RL) algorithm. Using the proposed architecture we develop an RL agent that can make driving decisions directly from the sensor data. We formulate the autonomous driving problem as a Markov Decision Process and propose different architectures using Deep Q -Networks for two types of sensor data - top view images of the autonomous vehicle (AV) and its surrounding vehicles and information on relative position and velocities of the surrounding vehicles w.r.t the AV. We consider a highway scenario and analyze the performance of the RL agent using the proposed architectures using the highway-env simulator. We compare the driving performance of the AV for both sensor types and discuss their efficacy under varying traffic densities.
Date of Conference: 13-16 June 2023
Date Added to IEEE Xplore: 17 July 2023
ISBN Information:
Conference Location: Bucharest, Romania

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