Self-Driving Cars
169 papers with code • 0 benchmarks • 15 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
These leaderboards are used to track progress in Self-Driving Cars
Libraries
Use these libraries to find Self-Driving Cars models and implementations
DDAD
Lost and Found
Shifts
Talk2Car
BDD-X
Argoverse 2 Motion Forecasting
SOD
HPD
OC
ELAS
Latest papers
Maximum diffusion reinforcement learning
The assumption that data are independent and identically distributed underpins all machine learning.
Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers
phoenixrider12/visual_failure_mitigation
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Our results show the efficacy of the proposed approach in identifying and handling system-level anomalies, outperforming methods such as prediction error-based detection, and ensembling, thereby enhancing the overall safety and robustness of autonomous systems.
PanopticNeRF-360: Panoramic 3D-to-2D Label Transfer in Urban Scenes
Moreover, PanopticNeRF-360 enables omnidirectional rendering of high-fidelity, multi-view and spatiotemporally consistent appearance, semantic and instance labels.
Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation
To ascertain the effectiveness of our approach, our SANet model achieved competitive results on the real-time CamVid and cityscape datasets.
DAD++: Improved Data-free Test Time Adversarial Defense
vcl-iisc/data-free-defense-at-test-time
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With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios.
NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation
This is useful in a wide range of real-world applications where collecting pixel-wise labels is not feasible in time or cost.
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels
We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output.
TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
Driveable Area Segmentation and Lane Detection are particularly important for safe and efficient navigation on the road.
Mass-Producing Failures of Multimodal Systems with Language Models
tsb0601/multimon
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NeurIPS 2023
Because CLIP is the backbone for most state-of-the-art multimodal systems, these inputs produce failures in Midjourney 5. 1, DALL-E, VideoFusion, and others.
NeRF-LiDAR: Generating Realistic LiDAR Point Clouds with Neural Radiance Fields
We verify the effectiveness of our NeRF-LiDAR by training different 3D segmentation models on the generated LiDAR point clouds.
