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  • A Comprehensive Evaluation of Four End-to-End AI Autopilots . . .
    End-to-end AI autopilots for autonomous driving systems have emerged as a promising alternative to traditional modular autopilots, ofering the potential to reduce development costs and mitigate defects arising from module composition
  • CARLA and AVL Collaboration: Ushering in a New Era of Off . . .
    CARLA, a free, open-source simulator designed to support the development of autonomous driving systems, continues to advance the industry AVL engineers enhance CARLA for simulation in rough terrain by coupling CARLA with AVL’s advanced vehicle dynamics software
  • HTLiang gt;Project gt;CARLA_RL
    To address this, CARLA's built-in autopilot was introduced as a baseline guide While the autopilot could navigate an empty town, its performance degraded significantly with traffic
  • Autonomous Vehicle Control in CARLA Challenge - ScienceDirect
    As shown, CARLA AD Challenge stablishes a benchmark to validate fully autonomous vehicles in complex urban environments by recreating hyper-realistic scenes, democratizing the accessibility to cutting edge validation technologies reducing time and cost
  • GitHub - aadimator CARLA-Plus: An open-source PGM-based . . .
    CARLA+ is an open-source autonomous driving simulation framework designed for modeling the dynamics of complex urban environments Leveraging the power of probabilistic graphical models (PGMs), CARLA+ provides a robust and flexible platform for simulating autonomous driving scenarios in uncontrolled urban environments
  • End-to-End Autonomous Driving in CARLA: A Survey | IEEE . . .
    In this survey, we discuss how CARLA-based state-of-the-art implementations address various issues encountered in end-to-end autonomous driving through various model inputs, outputs, architectures, and training paradigms
  • Conditional Vehicle Trajectories Prediction in CARLA Urban . . .
    In this work, we show that complex ur-ban situations can be handled with raw signal input and mid-level representation We build a hybrid end-to-mid ap-proach predicting trajectories for neighbor vehicles and for the ego vehicle with a conditional navigation goal





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