Multi-sensor vehicle testing: Recording inertial sensors via CAN bus in combination with recorded GNSS RF signals

Hünerbein, K. von; Argent, P.; Schulze, Timo

doi:10.1109/inertialsensors.2015.7314269

Cited by 2 publications

(4 citation statements)

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“…For the related content of enhanced map construction involved in this article, the research group has carried out a lot of related studies. For example, [22] proposed a digital map making method based on cardinal spline to realize an optimal balance between the efficiency and reliability requirements of road models, [23] proposed a modeling method for urban complex intersections, [20] proposed a simple 3D map modeling method including the height of roadside buildings, which can be directly applied to this research.…”

Section: E Enhanced Mapmentioning

confidence: 99%

“…(3) In general road modeling part, based on the initial road model, a gradual optimization algorithm as [22] is introduced to determine the final general road model. According to the degree of the change of road curvature, the reasonable control points and optimal tension parameters can be determined.…”

Section: E Enhanced Mapmentioning

confidence: 99%

“…Meanwhile, although RISS greatly reduces the number of sensors and thus reduces costs, automotive companies are very sensitive to hardware cost, and they more prefer to further reduce costs. On the other hand, as more vehicles are equipped with safety control systems [22], it can provide wheel speed, acceleration and other information via the vehicle's controller area network (CAN) bus without any additional cost. In other words, all the sensors needed to build 3D RISS can be obtained from CAN bus without any additional cost, which gives us a possible solution.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

Song

Ren

Jiang

et al. 2022

Mathematical Problems in Engineering

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To realize the effective positioning in urban canyons, an enhanced map-aided Global Positioning System (GPS)/three-dimensional (3D) reduced inertial sensor system (RISS) tightly combined positioning strategy is proposed. First, the 3D RISS is only based on the built-in controller area network (CAN). CAN bus sensor without additional sensors is first constructed to lower the cost. Then, a simple but effective enhanced map is created to assist positioning. Based on the map, a Kalman filtering (KF) tightly coupled method is proposed to fuse the 3D RISS with GPS information and to achieve the preliminary positioning. In KF-based preliminary positioning method, a simply observation noise variance optimization algorithm based on 2D enhanced map is proposed to improve KF method. In this algorithm, the value of the observation noise variance matrix is determined only according to the building plane information which is contained in the enhanced map. Further, a multiweight map matching algorithm is proposed for optimizing the initial positioning results. In this algorithm, factors such as distance, direction, road network topology, and lane change are considered and applied to map matching to further increase the positioning performance and form the final positioning results. Finally, the effectiveness of the strategy is proved by field test. The results show that this method has better accuracy and reliability than the conventional method.

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Section: E Enhanced Mapmentioning

confidence: 99%

Section: E Enhanced Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

Song

Ren

Jiang

et al. 2022

Mathematical Problems in Engineering

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show abstract

“…This limitation can be mitigated by the option to use 2 SSDs with 2 TB each and by the possibility to attach a 8 TB external memory. However, attaching external memory makes the setup more bulky and less easy to handle during a test drive [23].…”

Section: Benefitsmentioning

confidence: 99%

5.3 - Testing Acquisition of GPS / GNSS Location and Velocity to Improve Safety in Autonomous Driving

Hünerbein¹,

Lange²

2018

Proceeding - Ettc2018

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GPS (Global Positioning) and GNSS (Global Navigation Satellite Systems) yield very accurate positioning, velocity and timing, which are crucial for many ADAS (advanced driver assistance systems) and in autonomous driving. 3D locations can be as accurate as a few decimeters, with the help of additional correction methods. In addition a wide range of different sensors, among them optical, LIDAR and RADAR are used to detect obstacles and street marks, GNSS location and trajectory data can be exchanged via wireless data links with other cars (Car2Car) or sent to infrastructure (Car2X), in order to improve early detection of possible accidents, avoid drastic breaking events on the road ahead, traffic jams and detect vehicles around the corner, which are not visible to the driver due to obscuration. This element of telemetry in ADAS and autonomous driving serves to improve safety of all traffic participants and efficiency of driving. In order to guarantee safety of ADAS and autonomous driving, millions of test km need to be driven on different roads in diverse environments. Especially in city centers and mountaineous areas, GNSS reception can be impaired by obscuration of signals by buildings, bridges, vegetation and mountains and by signals reflected on even surfaces, called multipath. Furthermore, there are many sources of interference, potentially jamming, spoofing and meaconing GPS/GNSS receivers, especially on motorways and in cities. Testing can become very time consuming and costly, especially when driving all the required kms. Thus, it is more efficient to record the GPS/GNSS and interference signal environment during test drives with advanced record and replay systems, to be able to reproduce the realistic signal environment inside the laboratory, enabling repeatable and realistic tests. Such new record and replay systems are able to record GNSS signals on all frequency bands with a high bandwidth and resolution plus many additional sensors synchronously. In this paper we will present the use cases of GPS/ GNSS data in autonomous driving, Car2Car and Car2X, sources of errors and vulnerabilities for GPS/GNSS and appropriate test systems to allow for repeatable and realistic tests in the laboratory. The aim is to decrease time and money spent on testing and to improve the safety of driver assistance systems and autonomous driving.

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Multi-sensor vehicle testing: Recording inertial sensors via CAN bus in combination with recorded GNSS RF signals

Cited by 2 publications

References 0 publications

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

Enhanced Map-Aided GPS/3D RISS Combined Positioning Strategy in Urban Canyons

5.3 - Testing Acquisition of GPS / GNSS Location and Velocity to Improve Safety in Autonomous Driving

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