Lane-level Accuracy Research Articles

Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments

Autonomuous transportation systems require navigation performance with a high level of integrity. As Global Navigation Satellite System (GNSS) real-time kinematic (RTK) solutions are needed to ensure lane level accuracy of the whole system, these solutions should be trustworthy, which is often not the case in urban environments. Thus, the prediction of integrity for specific routes or trajectories is of interest. The carrier-to-noise density ratio (C/N0) reported by the GNSS receiver offers important insights into the signal quality, the carrier phase availability and subsequently the RTK solution integrity. The ultimate goal of this research is to investigate the predictability of the GNSS signal strength. Using a ray-tracing algorithm together with known satellite positions and 3D building models, not only the satellite visibility but also the GNSS signal propagation conditions at waypoints along an intended route are computed. Including antenna gain, free-space propagation as well as reflection and diffraction at surfaces and vegetation, the predicted C/N0 is compared to that recorded by an Septentrio Altus receiver during an experiment in an urban environment in Hannover. Although the actual gain pattern of the receiving antenna was unknown, good agreements were found with small offsets between measured and predicted C/N0.

Using Radio Technical Commission for Maritime Services Corrections in a Consumer-Grade Lane-Level Positioning System for Connected Vehicles

<div>Connected vehicle (CV) technology has the potential to greatly improve the safety, mobility, and environmental sustainability of traffic. Many CV applications require the vehicle position as input, which is primarily provided by global navigation satellite systems (GNSS). Although a large number of those applications (e.g., Intersection Movement Assist) require vehicle positioning to have lane-level accuracy, it has been shown that the type of positioning system typically used by CVs currently cannot provide consistent lane-level accuracy, even under open-sky conditions. In order to address this gap, we have evaluated an enhanced positioning system that adds little, if any, to the cost of the CV. It consists of a single-frequency real-time kinematic (RTK)-capable GNSS receiver onboard the vehicle, which utilizes Radio Technical Commission for Maritime Services (RTCM) differential corrections transmitted over dedicated short-range communications (DSRC) by the roadside infrastructure. Tests on a moving vehicle show that this system could provide lane-level accuracy over 95% of the time in open-sky conditions. These tests also show the DSRC to be an effective means of disseminating RTCM corrections, given the intersection spacings and communication ranges in the test. However, neither RTCM nor the more commonly used space-based augmentation system (SBAS) differential corrections appeared to improve the positioning accuracy of GNSS in urban canyons.</div>

On Mitigating the Effects of Multipath on GNSS Using Environmental Context Detection

Accurate, ubiquitous and reliable navigation can make transportation systems (road, rail, air and marine) more efficient, safer and more sustainable by enabling path planning, route optimization and fuel economy optimization. However, accurate navigation in urban contexts has always been a challenging task due to significant chances of signal blockage and multipath and non-line-of-sight (NLOS) signal reception. This paper presents a detailed study on environmental context detection using GNSS signals and its utilization in mitigating multipath effects by devising a context-aware navigation (CAN) algorithm that detects and characterizes the working environment of a GNSS receiver and applies the desired mitigation strategy accordingly. The CAN algorithm utilizes GNSS measurement variables to categorize the environment into standard, degraded and highly degraded classes and then updates the receiver’s tracking-loop parameters based on the inferred environment. This allows the receiver to adaptively mitigate the effects of multipath/NLOS, which inherently depend upon the type of environment. To validate the functionality and potential of the proposed CAN algorithm, a detailed study on the performance of a multi-GNSS receiver in the quad-constellation mode, i.e., GPS, BeiDou, Galileo and GLONASS, is conducted in this research by traversing an instrumented vehicle around an urban city and acquiring respective GNSS signals in different environments. The performance of a CAN-enabled GNSS receiver is compared with a standard receiver using fundamental quality indicators of GNSS. The experimental results show that the proposed CAN algorithm is a good contributor for improving GNSS performance by anticipating the potential degradation and initiating an adaptive mitigation strategy. The CAN-enabled GNSS receiver achieved a lane-level accuracy of less than 2 m for 53% of the total experimental time-slot in a highly degraded environment, which was previously only 32% when not using the proposed CAN.

Low-Cost Real-Time PPP GNSS Aided INS for CAV Applications

Many connected and autonomous vehicle (CAV) applications benefit from navigation technologies that reliably achieve lane-level accuracy. Global organizations have recently begun to provide real-time common-mode error corrections for Global Navigation Satellite Systems (GNSS) that enable this level of positioning accuracy using Precise Point Positioning (PPP). Incorporating an Inertial Measurement Unit (IMU) with a GNSS receiver can achieve this positioning performance reliably on moving platforms while providing a full state estimate, with high bandwidth at a high sampling rate. For commercial automotive applications cost is critical; therefore, this article focuses on commercial grade IMU’s and single-frequency GNSS code and Doppler measurements. This article presents and demonstrates a tightly-coupled PPP GNSS aided Inertial Navigation System (INS) using only publicly available real-time data sources. The article discusses the role of the INS in automotive applications and how the quality of the IMU affects the ability to achieve that role. The experimental results demonstrate positioning accuracy that surpasses the Society of Automotive Engineering (SAE) J2945 specification (horizontal error ≤1.5 m and vertical error ≤ 3 m 68%).

Using PPP Information to Implement a Global Real-Time Virtual Network DGNSS Approach

Differential GNSS (DGNSS) has been demonstrated to provide reliable, high-quality range correction information enabling real-time navigation with centimeter to sub-meter accuracy, which is required for applications such as connected and autonomous vehicles. However, DGNSS requires a local reference station near each user. For a continental or global scale implementation, this information dissemination approach would require a dense network of reference stations whose construction and maintenance would be prohibitively expensive. Precise Point Positioning (PPP) affords more flexibility as a public service for Global Navigation Satellite Systems (GNSS) receivers, but its State Space Representation (SSR) format is not supported by most receivers in the field or on the market. This article proposes a novel Virtual Network DGNSS (VNDGNSS) approach and an optimization algorithm that is key to its implementation. The approach capitalizes on the existing PPP infrastructure without the need for new physical reference stations. Specifically, no reference station is needed in the local vicinity of any user. By connecting to public GNSS SSR data services, a VNDGNSS server maintains current information about satellite code bias, satellite orbit and clock, and atmospheric models. Construction of the Radio Technical Commission for Maritime Services (RTCM) Observation Space Representation (OSR) messages from this SSR information requires both the signal time-of-transmission and the satellite position at that time which are consistent with the time-of-reception for each client. This article presents an algorithm to determine these quantities. Then the VNDGNSS computes and transmits RTCM OSR messages to user receivers. This approach achieves global dissemination coverage for real-time navigation without the need for additional local base stations near each user. The results of real-time stationary and moving platform evaluations are included, using u-blox M8P and ZED-F9P receivers. The performance surpasses the Society of Automotive Engineering (SAE) specification (68% of horizontal error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leqslant$</tex-math></inline-formula> 1.5 m and vertical error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\leqslant$</tex-math></inline-formula> 3 m) and shows significantly better horizontal performance than GNSS Open Service (OS). The moving tests also show better horizontal performance than the ZED-F9P receiver with Satellite Based Augmentation Systems (SBAS) enabled and achieve the lane-level accuracy (95% of horizontal errors less than 1 m).

Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy.

For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a important for performing complex driving maneuvers. Classical GNSS-based methods are usually not accurate enough to have lane-level localization to support the AV’s maneuvers. LiDAR-based localization can provide accurate localization. However, the price of LiDARs is still one of the big issues preventing this kind of solution from becoming wide-spread commodity. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.

Map-Matching-Based Localization Using Camera and Low-Cost GPS For Lane-Level Accuracy

For self-driving systems or autonomous vehicles (AVs), accurate lane-level localization is a necessity for performing complex driving maneuvers. Classical GNSS based methods are usually not accurate enough to have lane-level localization to support the AV’s maneuvers. LiDAR-based localization can provide accurate localization. However, the LiDAR price is still one of the big issues preventing this kind of solution from becoming wide-spread commodities. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system and a low-cost GPS to achieve high precision lane-level localization. Experiments in real-world and real-time demonstrate that the proposed method achieves good lane-level localization accuracy, outperforming solutions based on only GPS.

Lane-Level Map-Aiding Approach Based on Non-Lane-Level Digital Map Data in Road Transport Security

To prevent terror attacks in which trucks are used as weapons as happened in Nice or Berlin in 2016, the European Project Autonomous Emergency Maneuvering and Movement Monitoring for Road Transport Security (TransSec) was launched in 2018. One crucial point of this project is the development of a map-aiding approach for the localization of vehicles on digital maps, so that the information in digital map data can be used to detect prohibited driving maneuvers, such as off-road or wrong-way drivers. For example, a lane-level map-aiding approach is required for wrong-way driver detection. Navigation Data Standard (NDS) is one of the worldwide map standards developed by several automobile manufacturers. So far, there is no lane-level NDS map covers a large area, therefore, it was decided to use the latest available NDS map without lane level accuracy. In this paper, a lane-level map-aiding approach based on a non-lane-level NDS map is presented. Due to the inaccuracy of vehicle position and digital map the map-aiding does not always provide the correct results, so probabilities of off-road and wrong-way diver detection are estimated to support risk estimation. The performance of the developed map-aiding approach is comprehensively evaluated with both real and simulated trajectories.

Adding ears to intelligent connected vehicles by combining microphone arrays and high definition map

Ears are responsible for the sense of hearing, which is indispensable for a human to understand the environment. It is similar to an intelligent vehicle where acoustical data contains useful and valuable information to comprehend the environment to enable autonomous driving. One important application of acoustical data processing is to localize sound sources. However, due to the complexity of microphone array setup and the unstable localization performance against noises, few research articles tried to localize surrounding vehicles based on on-board microphone arrays. The main contribution is the first implementation to combine roof-mounted microphones and high definition maps as vehicle' s ears for online 3D localization of surround vehicles. This article designed a six-microphones sensor array dedicated to the on-board application and improved sound source localization algorithms using the observer technique to adapt to dynamic driving scenarios. A map-based localization enhancement method is also proposed to make the system more robust to noises and model errors. Feasibility tests are conducted with real vehicles in the scenarios of pass-by and overtaking. Experimental results validate the feasibility of using microphones to localize surrounding vehicles with lane-level accuracy.

Lane-Level Map-Matching Method for Vehicle Localization Using GPS and Camera on a High-Definition Map.

Accurate vehicle localization is important for autonomous driving and advanced driver assistance systems. Existing precise localization systems based on the global navigation satellite system cannot always provide lane-level accuracy even in open-sky environments. Map-based localization using high-definition (HD) maps is an interesting method for achieving greater accuracy. We propose a map-based localization method using a single camera. Our method relies on road link information in the HD map to achieve lane-level accuracy. Initially, we process the image—acquired using the camera of a mobile device—via inverse perspective mapping, which shows the entire road at a glance in the driving image. Subsequently, we use the Hough transform to detect the vehicle lines and acquire driving link information regarding the lane on which the vehicle is moving. The vehicle position is estimated by matching the global positioning system (GPS) and reference HD map. We employ iterative closest point-based map-matching to determine and eliminate the disparity between the GPS trajectories and reference map. Finally, we perform experiments by considering the data of a sophisticated GPS/inertial navigation system as the ground truth and demonstrate that the proposed method provides lane-level position accuracy for vehicle localization.

A Single-Frequency Real-Time Lane-Level Positioning Method for Vehicle Safety

The integrity and accuracy of positioning are crucial factors in vehicle safety. This article proposes an enhanced real-time array calibration (ERAC) technique based on real-time array error calibration with the Beidou Navigation Satellite System (BDS). Through fixed-point positioning tests and road tests, it is found that the ERAC method is superior to the traditional methods in terms of accuracy, stability, confidence, and the time required to reconstruct reliable points. The performance of the tested receiver is better than that of a commercial receiver with standard single-point positioning (SSP) in the context of multipath interference. Furthermore, the ERAC method meets the requirements for autonomous vehicle (AV) monitoring and public safety, especially in terms of the lane-level accuracy indicator, and its integrity indicators are greater than 95% to 97%. Additionally, the ERAC method has the potential to become an alternative solution for automatic vehicle safety monitoring in certain scenarios. This technique also has the ability to meet the wide positioning requirements of various vehicles and scenarios by adjusting its technical details.

Low-Cost Real-Time PPP/INS Integration for Automated Land Vehicles.

The last decade has witnessed a growing demand for precise positioning in many applications including car navigation. Navigating automated land vehicles requires at least sub-meter level positioning accuracy with the lowest possible cost. The Global Navigation Satellite System (GNSS) Single-Frequency Precise Point Positioning (SF-PPP) is capable of achieving sub-meter level accuracy in benign GNSS conditions using low-cost GNSS receivers. However, SF-PPP alone cannot be employed for land vehicles due to frequent signal degradation and blockage. In this paper, real-time SF-PPP is integrated with a low-cost consumer-grade Inertial Navigation System (INS) to provide a continuous and precise navigation solution. The PPP accuracy and the applied estimation algorithm contributed to reducing the effects of INS errors. The system was evaluated through two road tests which included open-sky, suburban, momentary outages, and complete GNSS outage conditions. The results showed that the developed PPP/INS system maintained horizontal sub-meter Root Mean Square (RMS) accuracy in open-sky and suburban environments. Moreover, the PPP/INS system could provide a continuous real-time positioning solution within the lane the vehicle is moving in. This lane-level accuracy was preserved even when passing under bridges and overpasses on the road. The developed PPP/INS system is expected to benefit low-cost precise land vehicle navigation applications including level 2 of vehicle automation which comprises services such as lane departure warning and lane-keeping assistance.

LaIF: A Lane-Level Self-Positioning Scheme for Vehicles in GNSS-Denied Environments

Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. We consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow us to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramer–Rao lower bound is also derived to provide a theoretical performance guideline. The performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway.

Improving Localization Accuracy in Connected Vehicle Networks Using Rao–Blackwellized Particle Filters: Theory, Simulations, and Experiments

A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost global navigation satellite system (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach in improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao–Blackwellized particle filter is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiver-specific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared with existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 m under open sky conditions.

Lane-Level Localization and Mapping in GNSS-Challenged Environments by Fusing Lidar Data and Cellular Pseudoranges

A method for achieving lane-level localization in global navigation satellite system (GNSS)-challenged environments is presented. The proposed method uses the pseudoranges drawn from unknown ambient cellular towers as an exclusive aiding source for a vehicle-mounted light detection and ranging (lidar) sensor. The following scenario is considered. A vehicle aiding its lidar with GNSS signals enters an environment where these signals become unusable. The vehicle is equipped with a receiver capable of producing pseudoranges to unknown cellular towers in its environment. These pseudoranges are fused through an extended Kalman filter to aid the lidar odometry, while estimating the vehicle's own state (3-D position and orientation) simultaneously with the position of the cellular towers and the difference between the receiver's and cellular towers’ clock error states (bias and drift). The proposed method is computationally efficient and is demonstrated to achieve lane-level accuracy in different environments. Simulation and experimental results with the proposed method are presented illustrating a close match between the vehicle's true trajectory and estimated using the cellular-aided lidar odometry over a 1 km trajectory. The proposed method yielded a 68% reduction in the 2-D position root mean-squared error (RMSE) over lidar odometry-only.

V2V System Congestion Control Validation and Performance

Major international automakers have considered the deployment of the 5.9 GHz dedicated short-range communications (DSRC) on their vehicle fleets for wireless connectivity. DSRC-enabled vehicle-to-vehicle (V2V) communication through broadcast of basic safety messages enables safety applications for crash warning and avoidance. However, in dense traffic conditions as the V2V deployment scales up, the resultant channel load increases and leads to channel congestion and may adversely affect the performance of the safety applications. The Society of Automotive Engineers J2945/1 standard that builds atop Institute of Electrical and Electronics Engineers (IEEE) 802.11p and IEEE 1609 standards provides the minimum performance requirements for V2V safety communications. Specifically, it provides a congestion control protocol for transmission rate and power adaptations to achieve robust performance in dense vehicular networks. The primary contribution of this paper is that using a congestion generation testbed that emulates channel congestion including a large number of remote vehicles, we can validate and test any V2V equipped vehicle for compliance with the J2945/1 standard. Our paper also demonstrates that under heavy congestion, even with 600 ms of inter-transmit time, a moving vehicle can be tracked to a lane-level accuracy.

GNSS Accuracy Improvement Using Rapid Shadow Transitions

Receiver modules in Global Navigation Satellite Systems (GNSS) are capable of providing positioning and velocity estimations that are sufficiently accurate for the purpose of road navigation. However, even in optimal open-sky conditions, GNSS-based positioning carries an average error of 2–4 m. This imposes an effective limitation on GNSS-based vehicle lane detection, a desired functionality for various navigation and safety applications. In this paper, we present a novel framework for lane-level accuracy using GNSS devices and 3-D shadow matching. The suggested framework is based on detection and analysis of rapid changes in navigation satellites' signal strength, which are caused by momentary blockages due to utility and light poles. A method for detecting such momentary changes between line of sight and non line of sight is presented, followed by a geometric algorithm that improves location accuracy of commercial GNSS devices. We have tested the framework's applicability using both simulations and field experiments. We provide the results of these tests and discuss receiver-side sampling rate requirements for high-performance lane-level positioning.

An Instantaneous Lane-Level Positioning Using DSRC Carrier Frequency Offset

A novel approach is proposed to enable vehicles to estimate their instantaneous position with lane-level accuracy, which is not achievable using Global Navigation Satellite Systems (GNSSs). This system can be used to enhance position-based applications such as Intelligent Transportation Systems (ITSs), including navigation and lane-level traffic guidance and warning. The system uses the carrier frequency offset (CFO) of the dedicated short-range communication (DSRC) signal, broadcast by two infrastructure beacons. The main advantages of the proposed method over other potential technologies such as radio-frequency identification (RFID) and buried loops are its simpler required infrastructure and functionality using vehicular communication platforms. Analysis of the proposed technique indicates acceptable reliability and performance. Empirical test results confirm the viability of the method.