Vehicle-to-Grid Optimization Considering Battery Aging

Electric vehicles (EVs) play a substantial role in reducing greenhouse gas emission and support a sustainable future. However, the increase of EV may lead to rising electricity demand and fluctuation. In this paper, the EV is proposed as a means to support the electricity grid via the vehicle-to-grid (V2G) technology. To reduce energy demand peaks, charging is planned during off-peak hours. Additionally, the EV battery may be used as a buffer to store energy during off-peak hours, and to supply energy to the grid during peak hours. Furthermore, grid frequency may be regulated by controlling the charging power. Since battery utilization will be increased during V2G operations, battery degradation is included in this study. A case study of Swedish households shows that the V2G is not only contributing to the stability of the grid, but may also help reducing the operating cost of an EV owner, even when battery degradation is considered

FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network

Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.Comment: 5 pages, 6 figures, 5 tables, IEEE Asilomar SS