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Found 4 papers, 2 papers with code
Efficient Baselines for Motion Prediction in Autonomous Driving
However, despite many approaches use simple ConvNets and LSTMs to obtain the social latent features, State-Of-The-Art (SOTA) models might be too complex for real-time applications when using both sources of information (map and past trajectories) as well as little interpretable, specially considering the physical information.
S$^3$-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object Detection
These methods typically use visual and depth representations to generate query points on objects, whose quality plays a decisive role in the detection accuracy.
Exploring Attention GAN for Vehicle Motion Prediction
The design of a safe and reliable Autonomous Driving stack (ADS) is one of the most challenging tasks of our era.
Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?
Autonomous driving is a challenging topic that requires complex solutions in perception tasks such as recognition of road, lanes, traffic signs or lights, vehicles and pedestrians.
