Datasets:

ivc-lrp
/

STSBench

@@ -46,7 +46,7 @@ We introduce STSBench, a scenario-based framework to benchmark the holistic unde
 # STSnu
-We leveraged our STSBench framework to construct a benchmark from the NuScenes dataset for current expert driving models about their spatio-temporal reasoning capabilities in traffic scenes.
 In particular, we automatically gathered and manually verified scenarios from all 150 scenes of the validation set, considering only annotated key frames.
 In contrast to prior benchmarks, focusing primarily on ego-vehicle actions that mainly occur in the front-view, STSnu evaluates spatio-temporal reasoning across a broader set of interactions and multiple views. This includes reasoning about other agents and their interactions with the ego-vehicle or with one another. To support this, we define four distinct scenario categories:

 # STSnu
+We leveraged our STSBench framework to construct a benchmark from the [nuScenes](https://arxiv.org/pdf/1903.11027) dataset for current expert driving models about their spatio-temporal reasoning capabilities in traffic scenes.
 In particular, we automatically gathered and manually verified scenarios from all 150 scenes of the validation set, considering only annotated key frames.
 In contrast to prior benchmarks, focusing primarily on ego-vehicle actions that mainly occur in the front-view, STSnu evaluates spatio-temporal reasoning across a broader set of interactions and multiple views. This includes reasoning about other agents and their interactions with the ego-vehicle or with one another. To support this, we define four distinct scenario categories: