LAUSR

Understanding pedestrian behaviors is crucial for a safe navigation of self-driving vehicles. However, pedestrians exhibit a large variety in their motion behaviors that are affected by interaction with the environment and other users of the road/sidewalk. Most of current models are limited to batch (offline) settings which requires to learn from the entire datasets. This letter presents a similarity-based model fusion algorithm, called SimFuse, for improving the prediction accuracy which enables autonomous agents to incrementally update their knowledge by communicating with other vehicles (V2V) by infrastructures (V2I). In the proposed framework, knowledge is shared in a compact form of “learned model” instead of raw data which provides a scalable sharing paradigm between agents. This work extends our prior work SILA [1] by providing multi model fusion of $n\ge 2$ models at the same time. We evaluate our algorithm in both intersection and non intersection scenarios and compare it with other baselines. The results show our algorithm outperforms state of the art in terms of Average Displacement Error at intersection scenarios and it has comparable result for non intersection scenarios with 3% improvement over SILA in ADE. The results also show that SimFuse updating time is up to 12 times faster than SILA with similar performance.