The actions of autonomous vehicle manufacturers and related industrial partners, as well as the interest from policy makers and researchers, point towards the likely initial deployment of autonomous vehicles as shared autonomous mobility services. Numerous studies are lately being published regarding Shared Autonomous Vehicle (SAV) applications and hence, it is imperative to have a comprehensive outlook, consolidating the existing knowledge base. This work comprehensively consolidates studies in the rapidly emerging field of SAV. The primary focus is the comprehensive review of the foreseen impacts, which are categorised into seven groups, namely (i) Traffic & Safety, (ii) Travel behaviour, (iii) Economy, (iv) Transport supply, (v) Land–use, (vi) Environment & (vii) Governance. Pertinently, an SAV typology is presented and the components involved in modelling SAV services are described. Issues relating to the expected demand patterns and a required suitable policy framework are explicitly discussed.

/pdf/shared-autonomous-vehicle-services-a-comprehensive-review-qacqjg2kag.pdf

Shared autonomous vehicle services: A comprehensive review

As one of the key enabling technologies for automated driving, High Definition (HD) Maps have become a major research focus in recent years. While increasing research effort has been directed toward HD Map development, a comprehensive review of the overall conceptual framework and development status is still lacking. In this study, we start with a brief review of the highlights of navigation map history, and then present an extensive literature review of HD Map development for automated driving, focusing on HD Map structure, functionalities, and accuracy requirements as well as standardisation aspects. In addition, this study conducts an analysis of HD Map-based vehicle localisation. The numerical results demonstrate the potential capabilities of HD Maps. Some recommendations for further investigation are made.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/7FFB4F68B9C27F4312AF8DCD553205FE/S0373463319000638a.pdf/div-class-title-high-definition-map-for-automated-driving-overview-and-analysis-div.pdf

High Definition Map for Automated Driving: Overview and Analysis

Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs. The objective of this study is to present an overview of both the state of the art and future perspectives of key technologies that are needed for future ICVs. It is a challenging task to review all related works and predict their future perspectives, especially for such a complex and interdisciplinary area of research. This article is organized to overview the ICV key technologies by answering three questions: what are the milestones in the history of ICVs; what are the electronic components needed for building an ICV platform; and what are the essential algorithms to enable intelligent driving? To answer the first question, the article has reviewed the history and the development milestones of ICVs. For the second question, the recent technology advances in electrical/electronic architecture, sensors, and actuators are presented. For the third question, the article focuses on the algorithms in decision making, as the perception and control algorithm are covered in the development of sensors and actuators. To achieve correct decision-making, there exist two different approaches: the principle-based approach and data-driven approach. The advantages and limitations of both approaches are explained and analyzed. Currently automotive engineers are concerned more with the vehicle platform technology, whereas the academic researchers prefer to focus on theoretical algorithms. However, only by incorporating elements from both worlds can we accelerate the production of high-level ICVs.

Intelligent and connected vehicles: Current status and future perspectives

With the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.

/pdf/an-ensemble-deep-learning-approach-for-driver-lane-change-1ogoen1cu4.pdf

An ensemble deep learning approach for driver lane change intention inference

Autonomous vehicles in the smart city era: An empirical study of adoption factors important for millennials

The development of intelligent vehicle systems has resulted in an increased need for a high-precision road map. However, conventional road maps that are used for vehicle navigation systems or geographical information systems (GISs) are insufficient to satisfy new requirements of intelligent vehicle systems such as autonomous driving. There are three primary road-map requirements for intelligent vehicle systems: centimeter-level accuracy, storage efficiency, and usability. However, no existing researches have met these three requirements simultaneously. In this paper, we propose a precise and efficient lane-level road-map generation system that conforms to the requirements all together. The proposed map-building process consists of three steps: 1) data acquisition, 2) data processing, and 3) road modeling. The road data acquisition and processing system captures accurate 3-D road geometry data by acquiring data with a mobile 3-D laser scanner. The road geometry data are then refined to extract meta information, and in the road modeling system, the refined data are represented as sets of piecewise polynomials to ensure storage efficiency and usability of the map. The proposed mapping system has been extensively tested and evaluated on a real urban road and highway. The experimental results show that the proposed mapping system outperforms conventional systems in terms of the road-map requirements.

Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

In this paper, we present a driverless taxi system for autonomous campus mobility services College campuses have unique mobility requirements in terms of layout, population, and demand and patterns It is typically recommended to minimize the presence of private automobiles on campuses due to teaching and research disturbances, visual degradation from parking provision, environmental pollution, and negative health effects As an alternative to private automobiles, shared mobility systems have been considered for both campus and urban transportation Conventional shuttle systems suffer from the first and last mile problem A bicycle and pedestrian friendly policy is not a generalizable solution for all geographic locations and campus layouts We suggest a driverless taxi service as an alternative point-to-point shared mobility system for campuses We have demonstrated the feasibility of this service on a 45-km campus road at Seoul National University The service has covered over 10 000 km autonomously since the first public demonstration was made in November 2015

Autonomous Campus Mobility Services Using Driverless Taxi

In this paper, we propose a new vehicle localization method based on topology matching in mutli-vehicle enviroment. Each vehicle is assumed to generate a local map which is a set of position measurements of nearby vehicles by using onboard low-cost GPS and ranging sensors, and share it with others by broadcasting via vehicle-to-vehicle(V2V) communication. When a vehicle receives multiple local maps from neighbors, it incorporates and fuses them with its own local map by using a local map matching algorithm. The proposed algorithm is based on the topology matching technique and the multi-sensor Kalman filter. Simulation results show that our method can extend the detection range and improve the position accuracy by 65% compared to conventional localization methods utilizing the Kalman filter with only onboard GPS measurements.

Cooperative localization based on topology matching

In an autonomous driving system, a precise reference map with static environment information is necessary to perform accurate localization and path planning. To build a static reference map, only the background parts in the scene must be extracted by removing dynamic objects in the scanned data. However, filtering dynamic objects out from the scene is currently an extremely difficult task. To solve this problem, we propose a ray binding method to detect precise free space and a background extraction method based on the free space information. The ray binding method is a novel approach to detect the free space of environments precisely and reliably by constructing a spherical triangular pyramid set from adjacent laser beams. This precise free space information can be utilized in the process of map building to remove dynamic objects automatically, enabling us to extract residual background points reliably. We experimentally verify that the proposed background extraction method using the free space information operates well in diverse real scenarios.

Woo-Sol Hur

Papers

Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems

Autonomous Campus Mobility Services Using Driverless Taxi

Cooperative localization based on topology matching

Precise free space detection and its application to background extraction