CARLA longest6
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Benchmarks
CARLA
Most implemented
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
PlanT: Explainable Planning Transformers via Object-Level Representations
CaRL: Learning Scalable Planning Policies with Simple Rewards
InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers
DriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
Coaching a Teachable Student
Papers
CLEAR: Closed-Loop Reinforcement Learning at Scale for End-to-End Autonomous Driving
End-to-end autonomous driving (E2E-AD) aims to directly map raw sensor information to driving actions. Recently, with the rapid advancement of multi-modal large language models (MLLMs), researchers have proposed the para…
Reinforcement LearningAutonomous DrivingCARLA longest6Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving
End-to-end planning systems for autonomous driving are improving rapidly, especially in closed-loop simulation environments like CARLA. Many such driving systems either do not consider uncertainty as part of the plan its…
Autonomous DrivingCARLA longest6Trajectory PlanningCaRL: Learning Scalable Planning Policies with Simple Rewards
We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail. RL, on the other hand, …
Autonomous DrivingCARLA longest6GPUImitation Learning+1DriveGPT4-V2: Harnessing Large Language Model Capabilities for Enhanced Closed-Loop Autonomous Driving
Multimodal large language models (MLLMs) possess the ability to comprehend visual images or videos, and show impressive reasoning ability thanks to the vast amounts of pretrained knowledge, making them highly suitabl…
Autonomous DrivingCARLA longest6Imitation LearningLanguage Modeling+2InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers
Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning and prediction as independent and ignore t…
Autonomous DrivingCARLA longest6PredictionDriveAdapter: Breaking the Coupling Barrier of Perception and Planning in End-to-End Autonomous Driving
End-to-end autonomous driving aims to build a fully differentiable system that takes raw sensor data as inputs and directly outputs the planned trajectory or control signals of the ego vehicle. State-of-the-art methods u…
Autonomous DrivingBench2DriveCARLA longest6