Autonomous vehicles necessitate a delicate balance between safety, efficiency, and user preferences in trajectory planning. Existing traditional or learning-based methods face challenges in adequately addressing all these aspects. In response, this paper proposes a novel component termed the Logical Guidance Layer (LGL), designed for seamless integration into autonomous driving trajectory planning frameworks, specifically tailored for highway scenarios. The LGL guides the trajectory planning with a local target area determined through scenario reasoning, scenario evaluation, and guidance area calculation. Integrating the Responsibility-Sensitive Safety (RSS) model, the LGL ensures formal safety guarantees while accommodating various user preferences defined by logical formulae. Experimental validation demonstrates the effectiveness of the LGL in achieving a balance between safety and efficiency, and meeting user preferences in autonomous highway driving scenarios.

Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost performance of the unweighted version. In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. The analysis immediately provides us explanations for understanding (1) quantitative effects of the class-wise reweighting mechanism: deterministic effectiveness for binary classification using logistic loss yet indeterministic for multi-class classification using softmax loss; (2) disadvantage of logistic loss for single-label multi-class classification via one-vs.-all approach, which is due to the averaging effect on predicted probabilities for the negative class (e.g., non-target classes) in the learning process. With the disadvantage and advantage of logistic loss disentangled, we thereafter propose a novel reweighted logistic loss for multi-class classification. Our simple yet effective formulation improves ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all) and turned out to be competitive with softmax loss. We evaluate our method on several benchmark datasets to demonstrate its effectiveness.

Toponym resolution, or grounding names of places to their actual locations, is an important problem in analysis of both historical corpora and present-day news and web content. Recent approaches have shifted from rule-based spatial minimization methods to machine learned classifiers that use features of the text surrounding a toponym. Such methods have been shown to be highly effective, but they crucially rely on gazetteers and are unable to handle unknown place names or locations. We address this limitation by modeling the geographic distributions of words over the earth's surface: we calculate the geographic profile of each word based on local spatial statistics over a set of geo-referenced language models. These geo-profiles can be further refined by combining in-domain data with background statistics from Wikipedia. Our resolver computes the overlap of all geo-profiles in a given text span; without using a gazetteer, it performs on par with existing classifiers. When combined with a gazetteer, it achieves state-of-the-art performance for two standard toponym resolution corpora (TR-CoNLL and Civil War). Furthermore, it dramatically improves recall when toponyms are identified by named entity recognizers, which often (correctly) find non-standard variants of toponyms.