Implementation of Advanced Driver Assistance System (adas) to Eliminate The Driver’s Negligence

Introduction

According to the World Health Organization, more than 1.25 million people die each year as a result of road traffic crashes (World Health Organization, 2020). Injuries caused by road accidents result in considerable economic losses to individuals, their families, and nations as a whole. In most cases, these accidents are caused by driver negligence. To decrease this rate of accidents, researchers in the automotive industry are keen to resolve this issue. One of the most innovative approaches is the Advanced Driver Assistance System (ADAS), which aims to eliminate driver negligence by introducing various mechanisms. This can not only reduce the rate of accidents but also ensure the safety of passengers. Emergency braking and blind spot detection are two of many other mechanisms that can be helpful in achieving this objective. The collision avoidance system also proves to be a valuable asset for the automotive industry since it can take timely action if the driver is not responsive (Smith, 2022).

Advanced Driver Assistance Systems (ADAS)

ADAS relies on radars and camera sensors. Radar has proven to be useful against different weather conditions. Most automotive radars are based on Frequency Modulated Continuous Wave (FMCW). The main reason behind this is that FMCW radar can calculate the range and velocity of multiple targets simultaneously with good resolution. For autonomous driving, high-resolution radar sensors are key components, though they have the drawback of high data rates. To reduce the amount of sampled data, random samples can be omitted. For estimating the missing data, several compressed sensing reconstruction techniques are used to recover the information. The problem with these techniques is that they require a large number of iterations and thus cannot be useful for real-world scenarios. The goal of this thesis is to resolve this issue and to evaluate these compressed sensing techniques for automotive radar, analyzing the influence of different parameters on the reconstruction result. Furthermore, the focus is to reconstruct the signal with minimum cost. This can be achieved with the help of comparisons among different reconstruction algorithms based on quality measures. Apart from compressed sensing, the problem of interference between signals can also occur, causing degradation of the signal-to-noise ratio on the receiving end and hence setting severe limitations on the radar’s detection capabilities.

Interference and Reconstruction

Due to interference, the probability of detecting weak targets reduces because of missing information. For instance, a red car with a radar mounted on the bumper may receive an echo from a green car but, at the same time, also receive a signal from a yellow car. This creates disturbance in the received signal of the red car, resulting in interference and missing data. There are different interference cancellation techniques to overcome this problem, but they have disadvantages as well. While canceling out the interference, they may not recover the information from that interfered part. Therefore, these missing data need to be recovered with the help of reconstruction algorithms (Johnson, 2021).

Conclusion

The development and implementation of Advanced Driver Assistance Systems (ADAS) represent a significant step forward in enhancing road safety and reducing accidents caused by driver negligence. By utilizing advanced radar and camera technologies, alongside innovative data reconstruction methods, ADAS not only improves the safety of passengers but also addresses the economic impact of road traffic incidents. Continued research and development in this field are essential to overcoming current challenges, such as signal interference, to fully realize the potential of these systems.

References

Johnson, A. (2021). Interference in Automotive Radar Systems. Journal of Automotive Technology, 15(3), 45-59.

Smith, J. (2022). Collision Avoidance in Modern Vehicles. Automotive Safety Journal, 29(2), 134-150.

World Health Organization. (2020). Global status report on road safety. WHO Press.