A blind spot detection and warning system based on millimeter wave radar for driver assistance

Liu, Guiru; Ming-zheng, Zhou; Wang, Lulin; Wang, Hai; Guo, Xiansheng

doi:10.1016/j.ijleo.2017.01.058

Cited by 38 publications

(20 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the analysis provided in this paper, the average value and the average variance of the slope of the datum point to its neighbours are referred to distinguish the effective vehicle trajectory points and the background noise. The average slope value and the standard deviation are calculated by Equa-tions (15) and (16), respectively:…”

Section: K-nearest Neighboursmentioning

confidence: 99%

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

Liu

Teng

Rai

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

In a new application scenario where the millimetre‐wave radar is installed above the road for detecting traffic flow in downward looking direction, the original data of the radar includes all kinds of background noises and false targets. In order to acquire effective vehicle trajectories, a two‐step abnormal data processing method for millimetre‐wave radar in traffic flow detection application is proposed. In the first step, the rational range of distance, angle and speed are studied, and proper thresholds are presented for reducing the samples of which the single parameter is with the obvious abnormality. Moreover, the nearest neighbour analysis method is used to further extract vehicle trajectories based on the similarity and slope characteristics of each sample to its neighbours. Taking actual detected data as samples, the weighting coefficients, similarity threshold, average slope threshold and standard deviation threshold are calibrated for the proposed nearest neighbour analysis method. The two‐step processing method presents a higher performance in extracting effective trajectory samples, and the ratio of noise points is reduced to 4.1%, compared with 239.9% in the original data sample. The proposed method can provide an effective reference for further applications, such as driving behaviour analysis and traffic flow parameter identification.

show abstract

Section: K-nearest Neighboursmentioning

confidence: 99%

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

Liu

Teng

Rai

et al. 2021

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Mapping the surroundings properly is key for accurate path planning and precise obstacle detection for autonomous vehicles. Two RADARs installed on the rear bumper could help to observe a blind spot when driving [22]. The blind spot locates in the rear left/right corner (Figure 3), an area which the driver cannot observe with either the external or internal reflection mirror.…”

Section: Radar/lidarmentioning

confidence: 99%

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

Маринов

2020

Sustainability

View full text Add to dashboard Cite

This paper reviews current developments and discusses some critical issues with obstacle detection systems for automated vehicles. The concept of autonomous driving is the driver towards future mobility. Obstacle detection systems play a crucial role in implementing and deploying autonomous driving on our roads and city streets. The current review looks at technology and existing systems for obstacle detection. Specifically, we look at the performance of LIDAR, RADAR, vision cameras, ultrasonic sensors, and IR and review their capabilities and behaviour in a number of different situations: during daytime, at night, in extreme weather conditions, in urban areas, in the presence of smooths surfaces, in situations where emergency service vehicles need to be detected and recognised, and in situations where potholes need to be observed and measured. It is suggested that combining different technologies for obstacle detection gives a more accurate representation of the driving environment. In particular, when looking at technological solutions for obstacle detection in extreme weather conditions (rain, snow, fog), and in some specific situations in urban areas (shadows, reflections, potholes, insufficient illumination), although already quite advanced, the current developments appear to be not sophisticated enough to guarantee 100% precision and accuracy, hence further valiant effort is needed.

show abstract

“…Driverless cars are expected to reduce the number of accidents happening due to human errors, and hence should be equipped with effective safety systems. These systems work on the inputs from cameras and sensors [1][2][3]. Major sensors are Light Detection and Ranging (LIDAR), Radio Detection and Ranging (RADAR), and Sound Detection and Ranging (SONAR).…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Based Position and Speed Estimation for Accident Avoidance in Driverless Cars

Thakurdesai

Aghav

2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

In the field of driverless cars, safety is the main concern. The safety systems for these cars are mainly dependent on the inputs of cameras and sensors like Light Detection and Ranging (LIDARs). Lidar is an essential component in driverless cars which creates 3D map of the surrounding and assists the car for driving. Lidar based safety systems are effective but are very expensive as the lidars are costly. Hence, there is a need to design a system which can assist the car effectively on the road, without using the lidar. This paper presents a proposed design of accident avoidance system for driverless cars, which uses computer vision techniques and works on the input of single video camera. It analyzes the video frames and shows accident warnings while on turn or lane change. The system is cost effective and can work with IoT devices having low computational power. To verify the performance of the system, we have performed experiments on various scenarios including cars at different positions moving with different speed and we obtained satisfactory results.

show abstract

A blind spot detection and warning system based on millimeter wave radar for driver assistance

Cited by 38 publications

References 31 publications

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

A Study on Recent Developments and Issues with Obstacle Detection Systems for Automated Vehicles

Computer Vision Based Position and Speed Estimation for Accident Avoidance in Driverless Cars

Contact Info

Product

Resources

About