docs: sync PDMS accuracy and J6M/J6P performance metrics

README.md

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 # E2E Diffusion Planner + HENet-tiny
 
-`e2e_planner` is an end-to-end autonomous-driving model on NAVSIM: an 8-camera BEV multitask perception front-end (HENet-tiny + single-frame BevFormer view transformer; dynamic/static 3D detection, occupancy, discrete map, online MapTR) feeding a BEV-feature-conditioned Diffusion Planner that outputs future ego trajectories. It covers the full pipeline from 5-stage float training → quantization calibration → HBIR export → HBM compilation → NAVSIM closed-loop metric evaluation.
+An end-to-end driving model for camera-based perception and short-horizon ego trajectory planning on NAVSIM.
+
+E2E Diffusion Planner + HENet-tiny combines an 8-camera BEV perception front-end with a diffusion planner conditioned on BEV features, ego state, and route. Perception and planning are trained and quantized as one graph for Horizon J6M / J6P deployment.
 
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 | March | latency (ms) | fps | Memory Usage (MB) |
 |---|---|---|---|
-| J6M | 99.14 (BPU 99.07) | 10.11 | 306.30 |
-| J6P | 37.84 (BPU 37.78) | 88.23 | 242.20 |
+| J6M | 104.66 (BPU 104.56) | 9.75 | 376.90 |
+| J6P | 53.25 (BPU 53.19) | 66.26 | 353.70 |
 
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 ### Core Design
 
-The model has two halves joined at the BEV feature: an 8-camera multitask perception front-end producing a `128×192` BEV grid, and a BEV-conditioned Diffusion Planner. HENet-tiny extracts multi-view camera features; a single-frame `SingleBevFormerViewTransformer` lifts them to BEV; five task heads (`DenseBEVSparseHead` dynamic/static, `BEVOCCHead2D` occupancy, `BEVFormerDet2DDecoder` discrete map, `MaptrDetDecoder` online map) share the BEV feature. The planner encodes the pooled BEV feature into 384 tokens, conditions a 3-layer DiT via a route-lane `RouteEncoder`, and denoises future ego waypoints over 5 reverse diffusion steps.
+Think of two stages glued on one BEV map. Cameras are encoded by HENet-tiny and lifted with a single-frame BevFormer into a forward-looking BEV grid; the same feature feeds detection, occupancy, and map heads for a consistent scene. A compact diffusion planner then reads that BEV, listens to the route, and denoises a few future ego waypoints—so planning stays conditioned on what perception actually sees, without a separate hand-off stack.
 
 - **Task type**: End-to-end planning (perception + planning joint).
 - **Perception backbone**: HENet-tiny (`block_nums=[4,3,8,6]`, `embed_dims=[64,128,192,384]`), initialized from the ImageNet-pretrained `henet_tinym` float checkpoint.