CARLA Leaderboard 2.0
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CARLA
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Hidden Biases of End-to-End Driving Datasets
CarLLaVA: Vision language models for camera-only closed-loop driving
Hidden Biases of End-to-End Driving Models
Papers
TaCarla: A comprehensive benchmarking dataset for end-to-end autonomous driving
Collecting a high-quality dataset is a critical task that demands meticulous attention to detail, as overlooking certain aspects can render the entire dataset unusable. Autonomous driving challenges remain a prominent ar…
CARLA Leaderboard 2.0Autonomous DrivingObject DetectionPlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving
Most recent work in autonomous driving has prioritized benchmark performance and methodological innovation over in-depth analysis of model failures, biases, and shortcut learning. This has led to incremental improvements…
CARLA Leaderboard 2.0Scene UnderstandingAutonomous DrivingRaw2Drive: Reinforcement Learning with Aligned World Models for End-to-End Autonomous Driving (in CARLA v2)
Reinforcement Learning (RL) can mitigate the causal confusion and distribution shift inherent to imitation learning (IL). However, applying RL to end-to-end autonomous driving (E2E-AD) remains an open problem for its tra…
Autonomous DrivingBench2DriveCARLA Leaderboard 2.0Imitation Learning+4Hidden Biases of End-to-End Driving Datasets
End-to-end driving systems have made rapid progress, but have so far not been applied to the challenging new CARLA Leaderboard 2.0. Further, while there is a large body of literature on end-to-end architectures and train…
Bench2DriveCARLA Leaderboard 2.0End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning
Dynamic and interactive traffic scenarios pose significant challenges for autonomous driving systems. Reinforcement learning (RL) offers a promising approach by enabling the exploration of driving policies beyond the con…
Autonomous DrivingCARLA Leaderboard 2.0Reinforcement Learning (RL)CarLLaVA: Vision language models for camera-only closed-loop driving
In this technical report, we present CarLLaVA, a Vision Language Model (VLM) for autonomous driving, developed for the CARLA Autonomous Driving Challenge 2.0. CarLLaVA uses the vision encoder of the LLaVA VLM and the LLa…
Autonomous DrivingBench2DriveCARLA Leaderboard 2.0Language Modeling+1