Mar 21, 2023 · In this paper, we propose VAD, an end-to-end vectorized paradigm for autonomous driving, which models the driving scene as a fully vectorized representation. The proposed vectorized paradigm has two significant advantages.

Mar 21, 2023 · We propose VAD, an end-to-end unified vectorized paradigm for autonomous driving. VAD models the driving scene as a fully vectorized representation, getting rid of computationally intensive dense rasterized representation and hand-designed post …

We propose VAD, an end-to-end unified vectorized paradigm for autonomous driving. VAD models the driving scene as a fully vectorized representation, getting rid of computationally intensive dense rasterized representation and hand-designed post-processing steps.

Nuplan offers ruled-based tags for each driving scenario. We perform the test in various aspects and store each with multiple metircs. For the detail, check test/scenario_test. We include comparison results between VADv2 and VAD in this repo.

Feb 20, 2024 · VADv2 takes multi-view image sequences as input in a streaming manner, transforms sensor data into environmental token embeddings, outputs the probabilistic distribution of action, and samples one action to control the vehicle.

In this work, we propose VAD (Vectorized Autonomous Driving), an end-to-end vectorized paradigm for au-tonomous driving. VAD models the scene in a fully vec-torized way (i.e., vectorized agent motion and map), getting rid of computationally intensive …

VADv2 takes multi-view image se-quences as input in a streaming manner, transforms sen-sor data into environmental token embeddings, outputs the probabilistic distribution of action, and samples one action to control the vehicle.

VADv2 takes multi-view image sequence as input in a streaming manner, transforms sensor data into environmental token embeddings, outputs the probabilistic distribution of action, and samples one action to control the vehicle. The probabilistic distribution of action is learned from large-scale driving demonstrations.

Feb 20, 2024 · VADv2 takes multi-view image sequences as input in a streaming manner, transforms sensor data into environmental token embeddings, outputs the probabilistic distribution of action, and samples one action to control the vehicle.

The paper VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning introduces a novel approach to autonomous driving by integrating probabilistic planning into an end-to-end driving model. Here are the key insights: