SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 3: Performance of CoBEVFlow and other baseline methods under the expectation of time interval from 0 to 500ms on IRV2V dataset. CoBEVFlow outperforms all the baseline methods and shows great robustness under any level of asynchrony.
Expectation of interval (ms)	0	100	200	300	400	500
Model / Metric	AP@0.50↑
Single	0.647
Late Fusion	0.828	0.638	0.478	0.371	0.324	0.308
V2VNet	0.811	0.747	0.710	0.663	0.626	0.591
V2X-ViT	0.781	0.737	0.692	0.641	0.598	0.575
DiscoNet	0.742	0.728	0.704	0.673	0.647	0.625
Where2comm	0.864	0.758	0.609	0.510	0.455	0.431
Where2comm+SyncNet	0.864	0.721	0.672	0.654	0.649	0.625
CoBEVFlow (ours)	0.864	0.841	0.834	0.831	0.812	0.815
Model / Metric	AP@0.70↑
Single	0.535
Late Fusion	0.751	0.385	0.285	0.235	0.219	0.223
V2VNet	0.744	0.607	0.540	0.480	0.439	0.408
V2X-ViT	0.630	0.577	0.545	0.511	0.489	0.479
DiscoNet	0.624	0.612	0.586	0.559	0.537	0.519
Where2comm	0.827	0.613	0.458	0.388	0.362	0.359
Where2comm+SyncNet	0.827	0.602	0.555	0.549	0.545	0.536
CoBEVFlow (ours)	0.827	0.781	0.761	0.757	0.714	0.687
We extended our experiments to include the V2XSet[5] dataset. V2XSet is a simulated dataset where each scenario involves at most one roadside unit and 2 to 4 vehicles as collaborative objects. The outcomes of these experiments are summarized in Table 5. Under time delays of  $300\mathrm{ms}$  and  $500\mathrm{ms}$ , the AP@0.70 scores achieved by CoBEVFlow are 0.776 and 0.713 respectively. These values surpass the best baseline methods by  $17.0\%$  and  $10.6\%$  respectively, and are notably higher than the results of single-object detection(0.556). This once again underscores CoBEVFlow's ability to maintain high levels of collaborative perception performance in scenarios involving temporal asynchrony.
Table 4: Performance of CoBEVFlow and other baseline methods under the expectation of time interval from 0 to 500ms on DAIR-V2X[14] dataset. CoBEVFlow outperforms all the baseline methods and shows great robustness under any level of asynchrony.
Expectation of interval (ms)	0	100	200	300	400	500
Model / Metric	AP@0.50↑
Single	0.674
Late Fusion	0.709	0.664	0.616	0.600	0.595	0.593
V2VNet	0.626	0.623	0.622	0.617	0.612	0.608
V2X-ViT	0.725	0.714	0.704	0.693	0.681	0.681
DiscoNet	0.645	0.630	0.620	0.606	0.597	0.590
Where2comm	0.807	0.739	0.603	0.698	0.686	0.676
Where2comm+SyncNet	0.807	0.719	0.706	0.695	0.687	0.679
CoBEVFlow (ours)	0.807	0.765	0.749	0.738	0.728	0.726
Model / Metric	AP@0.70↑
Single	0.587
Late Fusion	0.538	0.479	0.467	0.464	0.466	0.465
V2VNet	0.556	0.555	0.554	0.552	0.548	0.548
V2X-ViT	0.556	0.553	0.550	0.545	0.541	0.541
DiscoNet	0.553	0.537	0.530	0.523	0.519	0.518
Where2comm	0.662	0.603	0.587	0.577	0.569	0.566
Where2comm+SyncNet	0.662	0.602	0.588	0.587	0.584	0.580
CoBEVFlow (ours)	0.662	0.621	0.601	0.599	0.592	0.588
Table 5: We conduct experiments on the V2XSet dataset, comparing the performance of CoBEVFlow and other baseline methods under different expected time intervals. CoBEVFlow outperforms all the baseline methods and shows great robustness under any level of asynchrony.
Expectation of interval (ms)	0	300	500
Model / Metric	AP@0.50/AP@0.70 ↑
Single	0.720 / 0.556
Late Fusion	0.881/0.751	0.460/0.307	0.442/0.333
V2VNet	0.906/0.781	0.581/0.334	0.524/0.341
V2X-ViT	0.912/0.751	0.712/0.529	0.642/0.499
Where2comm	0.901/0.847	0.677/0.542	0.612/0.511
Where2comm+SyncNet	0.901/0.847	0.801/0.663	0.781/0.645
CoBEVFlow (ours)	0.901/0.847	0.871/0.776	0.841/0.713