The path inference filter: model-based low-latency map matching of probe vehicle data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 10 seconds and 2 minutes. We introduce a new class of algorithms, called altogether path inference filter (PIF), that maps GPS data in real time, for a variety of trade-offs and scenarios, and with a high throughput. Numerous prior approaches in map-matching can be shown to be special cases of the path inference filter presented in this article. We present an efficient procedure for automatically training the filter on new data, with or without ground truth observations. The framework is evaluated on a large San Francisco taxi dataset and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The path inference filter has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco, Sacramento, Stockholm and Porto.Comment: Preprint, 23 pages and 23 figure

Santiago urban dataset SUD: Combination of Handheld and Mobile Laser Scanning point clouds

Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles, and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research.Agencia Estatal de Investigación | Ref. PID2019-105221RB-C43Xunta de Galicia | Ref. ED481B-2019-061Xunta de Galicia | Ref. ED431C 2020/01Universidade de Vigo/CISU

Development of topographic maps and rear steering control for an agricultural vehicle through incorporation of posture and attitude measurements

The two articles contained in this work investigate the relationship between an agricultural vehicle\u27s posture and attitude with (a) its surroundings and (b) machine performance. In the first case, a self-propelled agricultural sprayer was equipped with four RTK DGPS receivers and an inertial measurement unit (IMU) to measure vehicle attitude and field elevation as the vehicle was driven across a field. Data was collected in a stop-and-go fashion as well as at three different speeds on a field area with varying topography. Using ordinary kriging, digital elevation models (DEMs) were interpolated from the elevation measurements and elevation plus attitude measurements. The resulting DEMs were compared to each other to evaluate the effect of including attitude measurement on DEM accuracy. At the widest swath width, the DEMs generated with attitude measurements had substantially lower error measures than those DEMs generated without attitude measurements. These results provide evidence that support the feasibility of using vehicle-based measurements collected during typical field operations for accurate DEM development. In the second case, a steering controller was designed and implemented on a self-propelled agricultural sprayer with four-wheel steering (4WS). The goals of this controller were to reduce the off-tracking error of the rear wheels and control turning radius during lateral shifts to reduce chemical application error. The vehicle was driven along marked courses of different shapes to test the steering controller\u27s performance. A computer simulation provided an estimate of chemical application rates across the spray boom during lateral shift maneuvers. During hillside operations, the controller was able to reduce the area damaged by the rear wheels from 107.35 m2 using two-wheel steer (2WS) to 0.32 m2 with Active Rear Steering (ARS) control. During 90-degree turns, the controller reduced the area damaged by rear wheels from 49.34 m2 in 2 WS to 1.15 m2 with ARS. This reduction in rear wheel off-tracking could lead to a reduction in crop damage through turns and during hillside operation, as well as reduced chemical application errors during turns

Facing ADAS validation complexity with usage oriented testing

International audienceValidating Advanced Driver Assistance Systems (ADAS) is a strategic issue, since such systems are becoming increasingly widespread in the automotive field. ADAS bring extra comfort to drivers, and this has become a selling point. But these functions, while useful, must not affect the general safety of the vehicle which is the manufacturer's responsibility. A significant number of current ADAS are based on vision systems, and applications such as obstacle detection and detection of pedestrians have become essential components of functions such as automatic emergency braking. These systems that preserve and protect road users take on even more importance with the arrival of the new Euro NCAP protocols. Therefore the robustness and reliability of ADAS functions cannot be neglected and car manufacturers need to have tools to ensure that the ADAS functions running on their vehicles operate with the utmost safety. Furthermore, the complexity of these systems in conjunction with the nearly infinite number of parameter combinations related to the usage profile of functions based on image sensors push us to think about testing optimization methods and tool standards to support the design and validation phases of ADAS systems. The resources required for the validation using current methods make them actually less and less adapted to new active safety features, which induce very strong dependability requirements. Today, to test the camera-based ADAS, test vehicles are equipped with these systems and are performing long hours of driving that can last for years. These tests are used to validate the use of the function and to verify its response to the requirements described in the specifications without considering the functional safety standard ISO26262

Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest

As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles