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.

scholarly journals Influence of adverse weather on drivers’ perceived risk during car following based on driving simulations

2019 ◽  
Vol 27 (4) ◽  
pp. 282-292 ◽  
Author(s):  
Chen Chen ◽  
Xiaohua Zhao ◽  
Hao Liu ◽  
Guichao Ren ◽  
Xiaoming Liu
Keyword(s):  
Perceived Risk ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Weather Conditions ◽  
Entropy Weight ◽  
Car Following ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Entropy Weight Method ◽  
Four Levels

Abstract Adverse weather has a considerable impact on the behavior of drivers, which puts vehicles and drivers in hazardous situations that can easily cause traffic accidents. This research examines how drivers’ perceived risk changes during car following under different adverse weather conditions by using driving simulation experiment. An expressway road scenario was built in a driving simulator. Eleven types of weather conditions, including clear sky, four levels of fog, four levels of rain and two levels of snow, were designed. Furthermore, to simulate the car-following behavior, three car-following situations were designed according to the motion of the lead car. Seven car-following indicators were extracted based on risk homeostasis theory. Then, the entropy weight method was used to integrate the selected indicators into an index to represent the drivers’ perceived risk. Multiple linear regression was applied to measure the influence of adverse weather conditions on perceived risk, and the coefficients were considered as indicators. The results demonstrate that both the weather conditions and road type have significant effects on car-following behavior. Drivers’ perceived risk tends to increase with the worsening weather conditions. Under conditions of extremely poor visibility, such as heavy dense fog, the measured drivers’ perceived risk is low due to the difficulties in vehicle operation and limited visibility.

Development of a Biologically-Based System for Detection and Tracking of Airborne Herbicides

1993 ◽  
Vol 7 (2) ◽  
pp. 404-410 ◽  
Author(s):  
Kassim Al-Khatib ◽  
Gaylord I. Mink ◽  
Guy Reisenauer ◽  
Robert Parker ◽  
Halvor Westberg ◽  
...  
Keyword(s):  
Computer Model ◽  
High Sensitivity ◽  
Weather Conditions ◽  
Weather Data ◽  
Indicator Plant ◽  
Field Exposure ◽  
Detection And Tracking ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Herbicide Drift

This study was designed to develop a protocol for using a biologically-based system to detect and tract airborne herbicides. Common bean, lentil, and pea were selected for their quasi-diagnostic sensitivity to chlorsulfuron, thifensulfuron, metsulfuron, tribenuron, paraquat, glyphosate, bromoxynil, 2,4-D, and dicamba. Plants were grown in the greenhouse at Prosser, WA, and placed at 25 exposure sites at weekly intervals between Apr. 2 and Oct. 15, 1991. After 1 wk of field exposure plants were brought back and observed for herbicide symptoms over a 28-d period. Symptoms that developed were compared with symptoms caused by disease, insects, adverse weather conditions, and herbicides applied at different rates under controlled conditions on these species. In addition, if herbicide symptoms were observed, herbicide spray records and weather data in the area were used in a computer model to determine the source of potential herbicide drift. This study demonstrates that indicator plant species selected for high sensitivity to herbicides can be used to monitor the occurrence of herbicide movement.

scholarly journals A Cost-Effective Photonic Radar Under Adverse Weather Conditions for Autonomous Vehicles by Incorporating a Frequency-Modulated Direct Detection Scheme

2021 ◽  
Vol 9 ◽  
Author(s):  
Abhishek Sharma ◽  
Sushank Chaudhary ◽  
Jyoteesh Malhotra ◽  
Muhammad Saadi ◽  
Sattam Al Otaibi ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Direct Detection ◽  
Signal To Noise Ratio ◽  
Autonomous Vehicle ◽  
Cost Effective ◽  
Weather Conditions ◽  
Atmospheric Conditions ◽  
Detection Scheme ◽  
Available Bandwidth ◽  
Adverse Weather

In recent years, there have been plenty of demands and growth in the autonomous vehicle industry, and thus, challenges of designing highly efficient photonic radars that can detect and range any target with the resolution of a few centimeters have been encountered. The existing radar technology is unable to meet such requirements due to limitations on available bandwidth. Another issue is to consider strong attenuation while working under diverse atmospheric conditions at higher frequencies. The proposed model of photonic radar is developed considering these requirements and challenges using the frequency-modulated direct detection technique and considering a free-space range of 750 m. The result depicts improved range detection in terms of received power and an acceptable signal-to-noise ratio and range under adverse climatic situations.

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 Design, Modelling, and Implementation of a Fuzzy Controller for an Intelligent Road Signaling System

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
José Manuel Lozano Domínguez ◽  
Tomás de J. Mateo Sanguino
Keyword(s):  
Traffic Accidents ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Weather Conditions ◽  
Prototype System ◽  
Road Maintenance ◽  
Good Test ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
High Level

Crossing points are not always 100% visible for drivers due to different factors (e.g., poor road maintenance, occlusion of vertical signs, and adverse weather conditions). USA estimated in 2015 the number of traffic accidents involving pedestrians and vehicles in 70,000 of whom 5,376 resulted in deceased people. To contribute in this field, this paper presents the design, implementation, and testing of a smart prototype system applied to pedestrian crossings—not regulated by semaphores—which try to reduce the accident rate on roads. The hardware and software system consists of a set of autonomous, intelligent, and wireless low-cost devices that generate a visual warning barrier perceived by drivers from a suitable distance when pedestrians traverse a crosswalk. In this way, drivers can reduce the speed of their vehicles and stop safely. The system’s intelligence is carried out by a fuzzy controller that performs sensory fusion at both low level and high level with various types of sensors from local and neighboring devices. The tests conducted have determined an average success of 94.64% and a precision of 100%, thus corresponding with a very good test according to a ROC analysis. As a result, the system proposed has been patented and extended to international PCT.

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.

Enhancing the Performance of Vehicle Passenger Detection under Adverse Weather Conditions Using Augmented Reality-Based Machine Learning Approach

2021 ◽  
pp. 036119812110312
Author(s):  
Jaeyun Lee ◽  
Sangcheol Kang ◽  
Jaedeok Lim ◽  
Seong Geon Kim ◽  
Changmo Kim
Keyword(s):  
Augmented Reality ◽  
Traffic Congestion ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Training Data ◽  
Weather Data ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Adverse Weather Conditions ◽  
Adverse Weather

In response to extreme traffic congestion in metropolitan areas that causes unnecessarily long travel times, high fuel consumption, and excessive greenhouse gas emissions, transportation agencies have implemented various strategies to mitigate traffic congestion. Managed lanes—one of the measures applied worldwide—provide benefits to road users and operating agencies by integrating advanced technologies such as electronic and dynamic tolling systems. However, those agencies already implementing or considering implementing the managed lane strategy are seeking a solution to effectively and properly charge toll rates based on vehicle occupancy and penalize violating vehicles. Vehicle passenger detection systems (VPDSs) have been developed and evaluated worldwide, but limitations still inhibit their full implementation. This study confirms that the performance of the deep learning algorithm, a core VPDS technology, declines under certain adverse weather conditions because of lack of training data sets. The performance of the “you only look once” (YOLOv3) model trained with a normal weather data set decreased by as much as 8.5% when it was tested for adverse weather conditions. In this study, augmented reality (AR) models are developed to enhance the accuracy of vehicle passenger detection (VPDA) by the VPDS by training the algorithm with AR images representing virtual adverse weather conditions. Models trained with AR image sets of various weather categories (fog, rain, and snow) attained VPDA enhanced by up to 7.9%. The final model significantly improves VPDA under adverse weather conditions. The proposed models could be considered for implementation with road weather information systems under adverse weather conditions.

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.

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