CARLA longest6
12 papers with code • 1 benchmarks • 1 datasets
longest6 is an evaluation benchmark for sensorimotor autonomous driving methods using the CARLA 0.9.10.1 simulator. It consists of 36 long routes in the publicly available Town 01-06 which, are populated with the maximum traffic density. The benchmark tests level 4 driving capabilities, methods are therefore allowed to train with data from the evaluation towns. Evaluation metrics follow the standard metrics from the CARLA leaderboard 1.0, except that stop sign infractions are not considered.
Benchmarks
These leaderboards are used to track progress in CARLA longest6
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CARLA
Most implemented papers
TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving
autonomousvision/transfuser
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At the time of submission, TransFuser outperforms all prior work on the CARLA leaderboard in terms of driving score by a large margin.
Learning to drive from a world on rails
This assumption greatly simplifies the learning problem, factorizing the dynamics into a nonreactive world model and a low-dimensional and compact forward model of the ego-vehicle.
NEAT: Neural Attention Fields for End-to-End Autonomous Driving
autonomousvision/neat
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ICCV 2021
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving.
Learning from All Vehicles
dotchen/LAV
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CVPR 2022
In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes.
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline
The two branches are connected so that the control branch receives corresponding guidance from the trajectory branch at each time step.
Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer
Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns.
PlanT: Explainable Planning Transformers via Object-Level Representations
Planning an optimal route in a complex environment requires efficient reasoning about the surrounding scene.
Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving
opendrivelab/thinktwice
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CVPR 2023
End-to-end autonomous driving has made impressive progress in recent years.
Hidden Biases of End-to-End Driving Models
autonomousvision/carla_garage
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ICCV 2023
End-to-end driving systems have recently made rapid progress, in particular on CARLA.
Coaching a Teachable Student
h2xlab/CaT
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CVPR 2023
We propose a novel knowledge distillation framework for effectively teaching a sensorimotor student agent to drive from the supervision of a privileged teacher agent.
