A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge

• URL: http://arxiv.org/abs/2010.12598v1
• Date: Fri, 23 Oct 2020 18:07:48 GMT
• Title: A Software Architecture for Autonomous Vehicles: Team LRM-B Entry in the First CARLA Autonomous Driving Challenge
• Authors: Luis Alberto Rosero, Iago Pacheco Gomes, J\'unior Anderson Rodrigues da Silva, Tiago Cesar dos Santos, Angelica Tiemi Mizuno Nakamura, Jean Amaro, Denis Fernando Wolf and Fernando Santos Os\'orio
• Abstract summary: This paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge.
• Score: 49.976633450740145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The objective of the first CARLA autonomous driving challenge was to deploy autonomous driving systems to lead with complex traffic scenarios where all participants faced the same challenging traffic situations. According to the organizers, this competition emerges as a way to democratize and to accelerate the research and development of autonomous vehicles around the world using the CARLA simulator contributing to the development of the autonomous vehicle area. Therefore, this paper presents the architecture design for the navigation of an autonomous vehicle in a simulated urban environment that attempts to commit the least number of traffic infractions, which used as the baseline the original architecture of the platform for autonomous navigation CaRINA 2. Our agent traveled in simulated scenarios for several hours, demonstrating his capabilities, winning three out of the four tracks of the challenge, and being ranked second in the remaining track. Our architecture was made towards meeting the requirements of CARLA Autonomous Driving Challenge and has components for obstacle detection using 3D point clouds, traffic signs detection and classification which employs Convolutional Neural Networks (CNN) and depth information, risk assessment with collision detection using short-term motion prediction, decision-making with Markov Decision Process (MDP), and control using Model Predictive Control (MPC).

Related papers

• Are you a robot? Detecting Autonomous Vehicles from Behavior Analysis [6.422370188350147]
  We present a framework that monitors active vehicles using camera images and state information in order to determine whether vehicles are autonomous. Essentially, it builds on the cooperation among vehicles, which share their data acquired on the road feeding a machine learning model to identify autonomous cars. Experiments show it is possible to discriminate the two behaviors by analyzing video clips with an accuracy of 80%, which improves up to 93% when the target state information is available.
  arXiv  Detail & Related papers  (2024-03-14T17:00:29Z)
• DME-Driver: Integrating Human Decision Logic and 3D Scene Perception in Autonomous Driving [65.04871316921327]
  This paper introduces a new autonomous driving system that enhances the performance and reliability of autonomous driving system. DME-Driver utilizes a powerful vision language model as the decision-maker and a planning-oriented perception model as the control signal generator. By leveraging this dataset, our model achieves high-precision planning accuracy through a logical thinking process.
  arXiv  Detail & Related papers  (2024-01-08T03:06:02Z)
• Traffic Flow Simulation for Autonomous Driving [5.39623346513589]
  This paper adopts the vehicle motion model based on cellular automata and the theory of bicycle intelligence to build the simulation environment of autonomous vehicle flow. The architecture of autonomous vehicles is generally divided into a perception system, decision system and control system.
  arXiv  Detail & Related papers  (2023-07-23T02:51:10Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• Deep Q-Network Based Decision Making for Autonomous Driving [1.0152838128195467]
  This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers.
  arXiv  Detail & Related papers  (2023-03-21T07:01:22Z)
• Tackling Real-World Autonomous Driving using Deep Reinforcement Learning [63.3756530844707]
  In this work, we propose a model-free Deep Reinforcement Learning Planner training a neural network that predicts acceleration and steering angle. In order to deploy the system on board the real self-driving car, we also develop a module represented by a tiny neural network.
  arXiv  Detail & Related papers  (2022-07-05T16:33:20Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits [81.22616193933021]
  The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021. It will benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. It is an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations.
  arXiv  Detail & Related papers  (2022-02-08T11:55:05Z)
• Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving [1.995792341399967]
  We develop a safe decision-making method for self-driving cars in a multi-lane, single-agent setting. The proposed approach utilizes deep reinforcement learning to achieve a high-level policy for safe tactical decision-making.
  arXiv  Detail & Related papers  (2021-05-13T19:17:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.