Hello all, I built a radar object classifier on RadarScenes, extending a prior single-scan classifier to accumulate observations over a tracked object's history instead of classifying each scan in isolation. A single RadarScenes object instance contains only about 2.9 radar points on average, very sparse. A single scan also can't capture temporal characteristics: RCS and micro-Doppler both vary continuously as an object moves. Pedestrians produce characteristic micro-Doppler from limb motion; different object classes show different RCS fluctuation patterns as aspect angle and scattering geometry change scan to scan. Accumulating observations gives both higher point density and provides temporal dynamics. Multi-scan baseline DeepReflecs encoder (Ulrich, Glaser & Timm, RadarConf 2021), PointNet style, per point shared weights, on single scans across car, large_vehicle, two_wheeler, pedestrian, pedestrian_group: 0.7370 macro F1. Using RadarScenes' persistent `track_id`, I build a causal, N=20, per track sliding-window buffer: - x_seq/y_seq: Global, odometry-corrected coordinates recentered per scan on the object centroid. Unlike x_cc/y_cc (car-frame coordinates that accumulate over time to form a trajectory). - Cross sensor buffer: whichever of the 4 sensors currently observe the track push to the same buffer. - Stride 1, causal: every new scan updates the buffer and produces a prediction. No future context, real time streaming compatible. - Each scan is encoded once by a frozen per scan encoder and cached - Fusion concatenates the causal GRU's hidden state (order aware) with an order-invariant pooled embedding (all N scans' points as one set, no sequence structure) through a small trained mlp head. Results Model Macro F1 Delta Single scan 0.7370 (baseline) 20 scan point pooling 0.8613 +0.1243 Causal GRU 0.8895 +0.0282 over pooling GRU + pooled embedding (fusion) 0.8897 +0.0002 over GRU, noise Pooling alone, no sequence model, no notion of scan order at all, recovers +0.1243 macro F1. The GRU adds a real but much smaller +0.0282 on top. Fusion adds nothing measurable beyond the GRU. Ablation Llarger GRUs, a Transformer, a state space model, point level self attention, all trained on the exact same frozen per scan embeddings, land inside a 0.86 to 0.89 band, a 0.03 spread. End to end fine tuning of the frozen encoder makes things slightly worse (about -0.002 to -0.003), not better. Conclusion In this setup, the largest gain comes from giving the model more observations of the same tracked object: 20-scan point pooling improves macro F1 from 0.7370 to 0.8613 without using scan order at all. Temporal modelling then provides a further, meaningful improvement. The causal GRU reaches 0.8895, adding +0.0282 over the pooled representation. So temporal ordering clearly contributes useful information; it just accounts for a smaller portion of the overall gain than observation accumulation. With the per-scan encoder frozen, the different sequence architectures tested, suggests that the quality of the per-scan representation is the bottleneck than the particular mechanism used to aggregate the sequence. Full report, every ablation, confusion matrix, coordinate frame reasoning: https://github.com/brunopinto900/radar-ml-autonomous-driving/blob/track-accumulation/final_report.md Thank you. &#32; submitted by &#32; /u/bruno_pinto90 [link] &#32; [comments]

Multi scan radar object classification on RadarScenes [P]

Gemini 4 Argon is Google's first frontier model in over seven months. It matches GPT-6 Astra in independent testing but can't keep up with Anthropic's Claude Opus 5.5. The per-token price is low, but Argon burns through more than twice as many tokens per task as Astra. Select testers get access first, with the API and paid tiers following later. The article Google Gemini 4 Argon closes the gap with OpenAI and Anthropic but doesn&#039;t take a clear lead appeared first on The Decoder.

Google Gemini 4 Argon closes the gap with OpenAI and Anthropic but doesn't take a clear lead

Google today revealed its next AI frontier model, which it's calling Gemini 4 Argon. The new model delivers "frontier performance in complex workflows across real-world software engineering, enterprise knowledge work like legal and finance, and cybersecurity defense," according to chief AI architect and Google DeepMind SVP Koray Kavukcuoglu. But the company is limiting access at first to a "set of trusted cyber defenders," and Kavukcuoglu says that Google is "actively engaged in the U.S. government's voluntary process for pre-release model access while we gradually expand access." Kavukcuoglu says Gemini 4 Argon is already powering Google's … Read the full story at The Verge.

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