DD-dimensional Bardeen-AdS black holes in Einstein-Gauss-Bonnet theory

We present a $D$-dimensional Bardeen like Anti-de Sitter (AdS) black hole solution in Einstein-Gauss-Bonnet (EGB) gravity, \textit{viz}., Bardeen-EGB-AdS black holes. The Bardeen-EGB-AdS black hole has an additional parameter due to charge ($e$), apart from mass ($M$) and Gauss-Bonnet parameter ($\alpha$). Interestingly, for each value of $\alpha$, there exist a critical $e = e_E$ which corresponds to an extremal regular black hole with degenerate horizons, while for $e< e_E$, it describes non-extremal black hole with two horizons. Despite the complicated solution, the thermodynamical quantities, like temperature ($T$), specific heat($C$) and entropy ($S$) associated with the black hole are obtained exactly. It turns out that the heat capacity diverges at critical horizon radius $r_+ = r_C$, where the temperature attains maximum value and the Hawking-Page transition is achievable. Thus, we have an exact $D$-dimensional regular black holes, when evaporates lead to a thermodynamical stable remnant.Comment: 25 pages, 48 figure

Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors

Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn multimodal behaviors of other agents from data. In this paper, we present an algorithm called multi-predictor fusion (MPF) that augments the performance of learning-based predictors by imbuing them with motion planners that are tasked with satisfying logic-based rules. MPF probabilistically combines learning- and rule-based predictors by mixing trajectories from both standalone predictors in accordance with a belief distribution that reflects the online performance of each predictor. In our results, we show that MPF outperforms the two standalone predictors on various metrics and delivers the most consistent performance