Ashwin Jadhav

Seong Hyeon Park, Gyubok Lee, Manoj Bhat et al.

Multi-agent trajectory forecasting in autonomous driving requires an agent to accurately anticipate the behaviors of the surrounding vehicles and pedestrians, for safe and reliable decision-making. Due to partial observability in these dynamical scenes, directly obtaining the posterior distribution over future agent trajectories remains a challenging problem. In realistic embodied environments, each agent's future trajectories should be both diverse since multiple plausible sequences of actions can be used to reach its intended goals, and admissible since they must obey physical constraints and stay in drivable areas. In this paper, we propose a model that synthesizes multiple input signals from the multimodal world|the environment's scene context and interactions between multiple surrounding agents|to best model all diverse and admissible trajectories. We compare our model with strong baselines and ablations across two public datasets and show a significant performance improvement over previous state-of-the-art methods. Lastly, we offer new metrics incorporating admissibility criteria to further study and evaluate the diversity of predictions. Codes are at: https://github.com/kami93/CMU-DATF.

Ashwin R Jadhav, T. Shankar

Wireless Sensor Network (WSN) consists of many individual sensors that are deployed in the area of interest. These sensor nodes have major energy constraints as they are small and their battery can't be replaced. They collaborate together in order to gather, transmit and forward the sensed data to the base station. Consequently, data transmission is one of the biggest reasons for energy depletion in WSN. Clustering is one of the most effective techniques for energy efficient data transmission in WSN. In this paper, an energy efficient cluster head selection algorithm which is based on Whale Optimization Algorithm (WOA) called WOA-Clustering (WOA-C) is proposed. Accordingly, the proposed algorithm helps in selection of energy aware cluster heads based on a fitness function which considers the residual energy of the node and the sum of energy of adjacent nodes. The proposed algorithm is evaluated for network lifetime, energy efficiency, throughput and overall stability. Furthermore, the performance of WOA-C is evaluated against other standard contemporary routing protocols such as LEACH. Extensive simulations show the superior performance of the proposed algorithm in terms of residual energy, network lifetime and longer stability period.