scholarly journals Passive Sensing of Electromagnetic Signature of Roadway Material for Lateral Positioning of Vehicle

2021 ◽  
Author(s):  
Sachindra Dahal ◽  
◽  
Jeffery Roesler ◽  
Keyword(s):  
Autonomous Vehicles ◽  
Concrete Slab ◽  
Weather Conditions ◽  
Concrete Surface ◽  
Lateral Position ◽  
Driver Assistance Systems ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Material Volume ◽  
Electromagnetic Signature

Autonomous vehicles (AV) and advanced driver-assistance systems (ADAS) offer multiple safety benefits for drivers and road agencies. However, maintaining the lateral position of an AV or a vehicle with ADAS within a lane is a challenge, especially in adverse weather conditions when lane markings are occluded. For significant penetration of AV without compromising safety, vehicle-to-infrastructure sensing capabilities are necessary, especially during severe weather conditions. This research proposes a method to create a continuous electromagnetic (EM) signature on the roadway, using materials compatible with existing paving materials and construction methods. Laboratory testing of the proposed concept was performed on notched concrete-slab specimens and concrete prisms containing EM materials. An induction-based eddy-current sensor and magnetometers were implemented to detect the EM signature. The detected signals were compared to evaluate the effects of sensor height above the concrete surface, type of EM materials, EM-material volume, material shape, and volume of EM concrete prisms. A layer of up to 2 in. (5.1 cm) of water, ice, snow, or sand was placed between the sensor and the concrete slab to represent adverse weather conditions. Results showed that factors such as sensor height, EM-material volume, EM dosage, types of the EM material, and shape of the EM material in the prism were significant attenuators of the EM signal and must be engineered properly. Presence of adverse surface conditions had a negligible effect, as compared to normal conditions, indicating robustness of the presented method. This study proposes a promising method to complement existing sensors’ limitations in AVs and ADAS for effective lane-keeping during normal and adverse weather conditions with the help of vehicle-to-pavement interaction.

scholarly journals Data Augmentation of Automotive LIDAR Point Clouds under Adverse Weather Situations

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4503
Author(s):  
Jose Roberto Vargas Rivero ◽  
Thiemo Gerbich ◽  
Boris Buschardt ◽  
Jia Chen
Keyword(s):  
Autonomous Vehicles ◽  
Data Augmentation ◽  
Synthetic Data ◽  
Real Data ◽  
Weather Conditions ◽  
Point Clouds ◽  
Training Data ◽  
Driver Assistance Systems ◽  
Solid Objects ◽  
Adverse Weather

In contrast to previous works on data augmentation using LIDAR (Light Detection and Ranging), which mostly consider point clouds under good weather conditions, this paper uses point clouds which are affected by spray. Spray water can be a cause of phantom braking and understanding how to handle the extra detections caused by it is an important step in the development of ADAS (Advanced Driver Assistance Systems)/AV (Autonomous Vehicles) functions. The extra detections caused by spray cannot be safely removed without considering cases in which real solid objects may be present in the same region in which the detections caused by spray take place. As collecting real examples would be extremely difficult, the use of synthetic data is proposed. Real scenes are reconstructed virtually with an added extra object in the spray region, in a way that the detections caused by this obstacle match the characteristics a real object in the same position would have regarding intensity, echo number and occlusion. The detections generated by the obstacle are then used to augment the real data, obtaining, after occlusion effects are added, a good approximation of the desired training data. This data is used to train a classifier achieving an average F-Score of 92. The performance of the classifier is analyzed in detail based on the characteristics of the synthetic object: size, position, reflection, duration. The proposed method can be easily expanded to different kinds of obstacles and classifier types.

The Impact of Adverse Weather Conditions on Autonomous Vehicles: How Rain, Snow, Fog, and Hail Affect the Performance of a Self-Driving Car

2019 ◽  
Vol 14 (2) ◽  
pp. 103-111 ◽  
Author(s):  
Shizhe Zang ◽  
Ming Ding ◽  
David Smith ◽  
Paul Tyler ◽  
Thierry Rakotoarivelo ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
The Impact

scholarly journals Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather

2021 ◽  
Vol 11 (7) ◽  
pp. 3018
Author(s):  
Shih-Lin Lin ◽  
Bing-Han Wu
Keyword(s):  
Kalman Filter ◽  
Autonomous Vehicles ◽  
Traffic Accidents ◽  
Autonomous Vehicle ◽  
Weather Conditions ◽  
Weather Data ◽  
Stereo Images ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
On The Road

A worldwide increase in the number of vehicles on the road has led to an increase in the frequency of serious traffic accidents, causing loss of life and property. Autonomous vehicles could be part of the solution, but their safe operation is dependent on the onboard LiDAR (light detection and ranging) systems used for the detection of the environment outside the vehicle. Unfortunately, problems with the application of LiDAR in autonomous vehicles remain, for example, the weakening of the echo detection capability in adverse weather conditions. The signal is also affected, even drowned out, by sensory noise outside the vehicles, and the problem can become so severe that the autonomous vehicle cannot move. Clearly, the accuracy of the stereo images sensed by the LiDAR must be improved. In this study, we developed a method to improve the acquisition of LiDAR data in adverse weather by using a combination of a Kalman filter and nearby point cloud denoising. The overall LiDAR framework was tested in experiments in a space 2 m in length and width and 0.6 m high. Normal weather and three kinds of adverse weather conditions (rain, thick smoke, and rain and thick smoke) were simulated. The results show that this system can be used to recover normal weather data from data measured by LiDAR even in adverse weather conditions. The results showed an effective improvement of 10% to 30% in the LiDAR stereo images. This method can be developed and widely applied in the future.

Effective Range Assessment of Lidar Imaging Systems for Autonomous Vehicles Under Adverse Weather Conditions with Stationary Vehicles

2021 ◽  
Author(s):  
Jamil Abdo ◽  
Spencer Hamblin ◽  
Genshe Chen
Keyword(s):  
Data Collection ◽  
Technology Implementation ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Imaging Systems ◽  
Extreme Conditions ◽  
Commercial Vehicles ◽  
Performance Quality ◽  
Adverse Weather Conditions ◽  
Adverse Weather

Abstract Light detection and ranging (Lidar) imaging systems are being increasingly used in autonomous vehicles. However, the final technology implementation is still undetermined as major automotive manufacturers are only starting to select providers for data collection units that can be introduced in commercial vehicles. Currently, testing for autonomous vehicles is mostly performed in sunny environments. Experiments conducted in good weather cannot provide information regarding performance quality under extreme conditions such as fog, rain, and snow. Under extreme conditions, many instances of false detection may arise because of the backscattered intensity, thereby reducing the reliability of the sensor. In this work, lidar sensors were tested in adverse weather to understand how extreme weather affects data collection. Testing setup and algorithms were developed for this purpose. The results are expected to provide technological validation for the commercial use of lidar in automated vehicles. The effective ranges of two popular lidar sensors were estimated under adverse weather conditions, namely, fog, rain, and snow. Results showed that fog severely affected lidar performance, and rain too had some effect on the performance. Meanwhile, snow did not affect lidar performance.

scholarly journals Ship Handling in Unprotected Waters: A Review of New Technologies in Escort Tugs to Improve Safety

2021 ◽  
Vol 2 (1) ◽  
pp. 46-62
Author(s):  
Santiago Iglesias-Baniela ◽  
Juan Vinagre-Ríos ◽  
José M. Pérez-Canosa
Keyword(s):  
High Performance ◽  
New Technologies ◽  
Weather Conditions ◽  
Hull Form ◽  
Best Available Technologies ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
New Type ◽  
Technological Challenge ◽  
And Performance

It is a well-known fact that the 1989 Exxon Valdez disaster caused the escort towing of laden tankers in many coastal areas of the world to become compulsory. In order to implement a new type of escort towing, specially designed to be employed in very adverse weather conditions, considerable changes in the hull form of escort tugs had to be made to improve their stability and performance. Since traditional winch and ropes technologies were only effective in calm waters, tugs had to be fitted with new devices. These improvements allowed the remodeled tugs to counterbalance the strong forces generated by the maneuvers in open waters. The aim of this paper is to perform a comprehensive literature review of the new high-performance automatic dynamic winches. Furthermore, a thorough analysis of the best available technologies regarding towline, essential to properly exploit the new winches, will be carried out. Through this review, the way in which the escort towing industry has faced this technological challenge is shown.

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