4 accepted papers
The inherent sequential modeling capabilities of autoregressive models make them a formidable baseline for end-to-end planning in autonomous driving. Nevertheless, their performance is constrained by a spatio-temporal misalignment, as the planner must condition future actions on past sensory data. T…
Generating trajectories from high-level commands is critical for autonomous driving, but prevailing methods suffer from a flaw we term semantic misalignment. By associating long trajectories with a single, static meta-action (e.g., “lane change”), these methods corrupt training data during maneuver
Optical sensors, such as the Far Infrared (FIR) sensor, have demonstrated advantages over traditional imaging. For example, 3D reconstruction in the FIR field captures the heat distribution of a scene that is invisible to RGB, aiding various applications like gas leak detection. However, less textur…
By adding exiting layers to the deep learning networks, early exit can terminate the inference earlier with accurate results. However, the passive decision-making of whether to exit or continue the next layer has to go through every pre-placed exiting layer until it exits. In addition, it is hard to…