Laser and LIDAR in a System for Visibility Distance Estimation in Fog Conditions

Self Cite

In mobile systems, fog, rain, snow, haze, and sun glare are natural phenomena that can be very dangerous for drivers. In addition to the visibility problem, the driver must face also the choice of speed while driving. The main effects of fog are a decrease in contrast and a fade of color. Rain and snow cause also high perturbation for the driver while glare caused by the sun or by other traffic participants can be very dangerous even for a short period. In the field of autonomous vehicles, visibility is of the utmost importance. To solve this problem, different researchers have approached and offered varied solutions and methods. It is useful to focus on what has been presented in the scientific literature over the past ten years relative to these concerns. This synthesis and technological evolution in the field of sensors, in the field of communications, in data processing, can be the basis of new possibilities for approaching the problems. This paper summarizes the methods and systems found and considered relevant, which estimate or even improve visibility in adverse weather conditions. Searching in the scientific literature, in the last few years, for the preoccupations of the researchers for avoiding the problems of the mobile systems caused by the environmental factors, we found that the fog phenomenon is the most dangerous. Our focus is on the fog phenomenon, and here, we present published research about methods based on image processing, optical power measurement, systems of sensors, etc.

Section: Onboard Sensors and Systemsmentioning

confidence: 99%

Section: Observations and Future Researchmentioning

confidence: 99%

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Visibility Enhancement and Fog Detection: Solutions Presented in Recent Scientific Papers with Potential for Application to Mobile Systems

Miclea

Ungureanu

Sandru

et al. 2021

Self Cite

“…Machine learning has been used in the creation of autonomous vehicles. Since conducting these experiments with real automobiles could be expensive and/or dangerous, simulators like CARLA ( ) have been developed, which allow for the validation and training of autonomous driving systems [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Other simulators like TORCS ( ) have been used with entertainment purposes, but also as AI research platforms [ 38 ].…”

Section: State Of Research On Intelligent Agentsmentioning

confidence: 99%

Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles

Urrea

Garrido

Kern

2021

This paper presents the results of the design, simulation, and implementation of a virtual vehicle. Such a process employs the Unity videogame platform and its Machine Learning-Agents library. The virtual vehicle is implemented in Unity considering mechanisms that represent accurately the dynamics of a real automobile, such as motor torque curve, suspension system, differential, and anti-roll bar, among others. Intelligent agents are designed and implemented to drive the virtual automobile, and they are trained using imitation or reinforcement. In the former method, learning by imitation, a human expert interacts with an intelligent agent through a control interface that simulates a real vehicle; in this way, the human expert receives motion signals and has stereoscopic vision, among other capabilities. In learning by reinforcement, a reward function that stimulates the intelligent agent to exert a soft control over the virtual automobile is designed. In the training stage, the intelligent agents are introduced into a scenario that simulates a four-lane highway. In the test stage, instead, they are located in unknown roads created based on random spline curves. Finally, graphs of the telemetric variables are presented, which are obtained from the automobile dynamics when the vehicle is controlled by the intelligent agents and their human counterpart, both in the training and the test track.

“…For this reason, several studies are currently focused on trying to solve this problem. Some of the solutions being worked on are based on improving pedestrian detection algorithms in this type of low visibility environment [ 29 , 30 , 31 ], and others focus on using other types of systems to obtain information, such as the use of LIDAR [ 32 , 33 ] or infrared sensors [ 34 , 35 ], or on fusing images obtained from the classic RGB camera with other detection systems, such as thermal cameras [ 36 ], LIDAR [ 37 ], or an array of microphones [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System

Izquierdo

Val

Villacorta

2021

Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.