Advances in Automobile Engineering
Open Access

Automobile Saving Lives Equipped with Crash Detection System

Thompson Richard^{* *}Correspondence: Thompson Richard, Department of Mechatronics, The University of Auckland, Auckland, New Zealand, Email:

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About the Study

Automobile accidents have become a major cause of concern around the world, leading to injuries, fatalities, and economic losses. In recent years, advanced crash detection systems have been developed and implemented to prevent or reduce the severity of accidents. These systems use various sensors and technologies to detect potential collisions and take corrective actions to prevent them. This article will explore the advancements in crash detection systems in the automobile industry. There are various types of crash detection systems, each with own set of sensors and technologies.

Forward Collision Warning (FCW) System: This system uses sensors to detect the distance between the vehicle and the object in front of it. If the distance reduces to a dangerous level, the system warns the driver with audible and visual signals to take corrective action.

Automatic Emergency Braking (AEB) System: This system uses sensors to detect potential collisions and automatically applies the brakes to prevent or reduce the severity of the impact.

Lane Departure Warning (LDW) System: This system uses cameras and sensors to detect when the vehicle is drifting out of its lane. It warns the driver with audible and visual signals to bring the vehicle back to its lane.

Blind Spot Detection (BSD) System: This system uses sensors to detect vehicles in the blind spot of the driver. It warns the driver with visual signals or audible alarms if there is a vehicle in the blind spot.

Rear Cross-Traffic Alert (RCTA) System: This system uses sensors to detect vehicles approaching from the side or rear when the vehicle is reversing. It warns the driver with audible and visual signals to prevent collisions.

Advancements in Crash Detection Systems: The advancements in crash detection systems have made them more efficient and reliable.

The following are some of the recent advancements:

Integration of Artificial Intelligence (AI): The integration of AI into crash detection systems has made them more intelligent and responsive. AI algorithms analyses sensor data and predict potential collisions with high accuracy. This has led to the development of systems that can detect and respond to potential collisions even before the driver is aware of them.

Use of Multiple Sensors: Earlier crash detection systems used a single sensor to detect potential collisions. However, modern systems use multiple sensors such as cameras, radar, and LiDAR to provide a more comprehensive view of the surroundings. This has led to increased accuracy and reliability of crash detection systems.

Vehicle-to-Vehicle (V2V) Communication: V2V communication enables vehicles to exchange information about their location, speed, and direction. This information can be used by the crash detection systems to predict potential collisions and take corrective actions.

Pedestrian Detection: The latest crash detection systems can also detect pedestrians and cyclists in the vicinity of the vehicle. This has led to the development of systems that can prevent collisions with vulnerable road users.

Conclusion

The advancements in crash detection systems have made driving safer and more efficient. These systems can detect potential collisions and take corrective actions to prevent them, thereby reducing the severity of accidents.

With the integration of AI, multiple sensors, V2V communication, and pedestrian detection, crash detection systems have become more intelligent and reliable. The continued development and implementation of these systems will play a crucial role in improving road safety and reducing the number of accidents on the road.