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1. Lane Marking Verification for High Definition Map Maintenance Using Crowdsourced Images

   Binbin Li, Dezhen Song (October 2020, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, Oct. 25-29, 2020)

   null (Ed.)

   Autonomous vehicles often rely on high-definition (HD) maps to navigate around. However, lane markings (LMs) are not necessarily static objects due to wear \& tear from usage and road reconstruction \& maintenance. Therefore, the wrong matching between LMs in the HD map and sensor readings may lead to erroneous localization or even cause traffic accidents. It is imperative to keep LMs up-to-date. However, frequently recollecting data with dedicated hardware and specialists to update HD maps is not only cost-prohibitive but also unviable. Here we propose to utilize crowdsourced images from multiple vehicles at different times to help verify LMs for HD map maintenance. We obtain the LM distribution in the image space by considering the camera pose uncertainty in perspective projection. Both LMs in HD map and LMs in the image are treated as observations of LM distributions which allow us to construct posterior conditional distribution (a.k.a Bayesian belief functions) of LMs from either sources. An LM is consistent if belief functions from the map and the image satisfy statistical hypothesis testing. We further extend the Bayesian belief model into a sequential belief update using crowdsourced images. LMs with a higher probability of existence are kept in the HD map whereas those with a lower probability of existence are removed from the HD map. We verify our approach using real data. Experimental results show that our method is capable of verifying and updating LMs in the HD map. 

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2. Comparing Directed and Weighted Road Maps

   https://doi.org/10.1007/978-3-319-89593-2_4  

   Bittner, Alyson; Fasy, Brittany Terese; Grudzien, Maia; Hajra, Sayonita Ghosh; Huang, Jici; Pelatt, Kristine; Thatcher, Courtney; Tumurbaatar, Altansuren; Wenk, Carola (July 2018, Association for Women in Mathematics series)

   With the increasing availability of GPS trajectory data, map construction algorithms have been developed that automatically construct road maps from this data. In order to assess the quality of such (constructed) road maps, the need for meaningful road map comparison algorithms becomes increasingly important. Indeed, different approaches for map comparison have been recently proposed; however, most of these approaches assume that the road maps are modeled as undirected embedded planar graphs. In this paper, we study map comparison algorithms for more realistic models of road maps: directed roads as well as weighted roads. In particular, we address two main questions: how close are the graphs to each other, and how close is the information presented by the graphs (i.e., traffic times, trajectories, and road type)? We propose new road network comparisons and give illustrative examples. Furthermore, our approaches do not only apply to road maps but can be used to compare other kinds of graphs as well. 

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3. On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data

   https://doi.org/10.1109/BigData55660.2022.10020390  

   Talusan, Jose Paolo; Mukhopadhyay, Ayan; Freudberg, Dan; Dubey, Abhishek (December 2022, 2022 IEEE International Conference on Big Data (Big Data))

   The ability to accurately predict public transit ridership demand benefits passengers and transit agencies. Agencies will be able to reallocate buses to handle under or over-utilized bus routes, improving resource utilization, and passengers will be able to adjust and plan their schedules to avoid overcrowded buses and maintain a certain level of comfort. However, accurately predicting occupancy is a non-trivial task. Various reasons such as heterogeneity, evolving ridership patterns, exogenous events like weather, and other stochastic variables, make the task much more challenging. With the progress of big data, transit authorities now have access to real-time passenger occupancy information for their vehicles. The amount of data generated is staggering. While there is no shortage in data, it must still be cleaned, processed, augmented, and merged before any useful information can be generated. In this paper, we propose the use and fusion of data from multiple sources, cleaned, processed, and merged together, for use in training machine learning models to predict transit ridership. We use data that spans a 2-year period (2020-2022) incorporating transit, weather, traffic, and calendar data. The resulting data, which equates to 17 million observations, is used to train separate models for the trip and stop level prediction. We evaluate our approach on real-world transit data provided by the public transit agency of Nashville, TN. We demonstrate that the trip level model based on Xgboost and the stop level model based on LSTM outperform the baseline statistical model across the entire transit service day. 

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4. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   https://doi.org/10.1109/MT-ITS49943.2021.9529289  

   Jiang, Liming; Xie, Yuanchang; Wen, Xiao; Chen, Danjue; Li, Tienan; Evans, Nicholas G. (January 2021, 2021 7th International Conference on Models and Technologies for Intelligent Transportation Systems)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using real-world vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically. 

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5. Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles – A Deep Reinforcement Learning Approach

   Jiang, L (June 2021, Proceedings of the 7th International IEEE Conference on Models and Technologies for Intelligent Transportation Systems)

   null (Ed.)

   Stop-and-go traffic poses significant challenges to the efficiency and safety of traffic operations, and its impacts and working mechanism have attracted much attention. Recent studies have shown that Connected and Automated Vehicles (CAVs) with carefully designed longitudinal control have the potential to dampen the stop-and-go wave based on simulated vehicle trajectories. In this study, Deep Reinforcement Learning (DRL) is adopted to control the longitudinal behavior of CAVs and real-world vehicle trajectory data is utilized to train the DRL controller. It considers a Human-Driven (HD) vehicle tailed by a CAV, which are then followed by a platoon of HD vehicles. Such an experimental design is to test how the CAV can help to dampen the stop-and-go wave generated by the lead HD vehicle and contribute to smoothing the following HD vehicles’ speed profiles. The DRL control is trained using realworld vehicle trajectories, and eventually evaluated using SUMO simulation. The results show that the DRL control decreases the speed oscillation of the CAV by 54% and 8%-28% for those following HD vehicles. Significant fuel consumption savings are also observed. Additionally, the results suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically. 

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