LaneSegNet: Map Learning with Lane Segment Perception for Autonomous Driving

A map, as crucial information for downstream applications of an autonomous driving system, is usually represented in lanelines or centerlines. However, existing literature on map learning primarily focuses on either detecting geometry-based lanelines or perceiving topology relationships of centerlines. Both of these methods ignore the intrinsic relationship of lanelines and centerlines, that lanelines bind centerlines. While simply predicting both types of lane in one model is mutually excluded in learning objective, we advocate lane segment as a new representation that seamlessly incorporates both geometry and topology information. Thus, we introduce LaneSegNet, the first end-to-end mapping network generating lane segments to obtain a complete representation of the road structure. Our algorithm features two key modifications. One is a lane attention module to capture pivotal region details within the long-range feature space. Another is an identical initialization strategy for reference points, which enhances the learning of positional priors for lane attention. On the OpenLane-V2 dataset, LaneSegNet outperforms previous counterparts by a substantial gain across three tasks, \textit{i.e.}, map element detection (+4.8 mAP), centerline perception (+6.9 DET$_l$), and the newly defined one, lane segment perception (+5.6 mAP). Furthermore, it obtains a real-time inference speed of 14.7 FPS. Code is accessible at https://github.com/OpenDriveLab/LaneSegNet.Comment: Accepted in ICLR 202

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmarks | OpenLane-V2 Dataset: https://github.com/OpenDriveLab/OpenLane-V

Association between Urban Educational Policies and Migrant Children’s Social Integration in China: Mediated by Psychological Capital

China’s urban educational policies have been established to solve the problems of potential discrimination and inequitable access to education, disrupting migrant children who move from rural areas to urban cities and who tend to suffer from a range of mental health issues. However, little is known regarding how China’s urban educational policies affect migrant children’s psychological capital and social integration. This paper aims to explore the effect of urban education policies on improving migrant children’s psychological capital level in China. The second objective of this paper is to examine whether policies can encourage them to integrate into urban society in a positive way. This paper thoroughly analyzes the impact of China’s urban educational policies on three dimensions of social integration of migrant children (identification, acculturation, and psychological integration), and also verifies the mediating effects of psychological capital on the relationships between these variables. The subjects of this study are 1770 migrant children in grades 8–12, who are sourced from seven coastal cities in China. Multiple regression analysis and mediation effect tests are employed to analyze the data. This study reveals that migrant children’s identification with educational policies has a significant positive impact on their psychological capital. Psychological capital has partial mediating effects on the relationship between identification with educational policies and the three dimensions of social integration. In other words, identification with educational policies indirectly affects the process of social integration of migrant children through psychological capital. Based on this, for the purpose of promoting the positive impacts of educational policies of inflow cities on the social integration of migrant children, this study makes the following recommendations: (a) at the micro-level, the psychological capital of individual migrant children should be enhanced; (b) at the meso-level, the partnerships between migrant children and urban children should be taken seriously; and (c) at the macro-level, the urban educational policies related to migrant children should be improved. This paper not only makes policy recommendations for improving the educational policies of inflow cities, but also offers a Chinese perspective on the research related to the tricky issue facing all countries around the world, the social integration of migrant children