Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Abstract

The market for autonomous vehicles (AV) is expected to experience significant growth over the coming decades and to revolutionize the future of transportation and mobility. The AV is a vehicle that is capable of perceiving its environment and perform driving tasks safely and efficiently with little or no human intervention and is anticipated to eventually replace conventional vehicles. Self-driving vehicles employ various sensors to sense and perceive their surroundings and, also rely on advances in 5G communication technology to achieve this objective. Sensors are fundamental to the perception of surroundings and the development of sensor technologies associated with AVs has advanced at a significant pace in recent years. Despite remarkable advancements, sensors can still fail to operate as required, due to for example, hardware defects, noise and environment conditions. Hence, it is not desirable to rely on a single sensor for any autonomous driving task. The practical approaches shown in recent research is to incorporate multiple, complementary sensors to overcome the shortcomings of individual sensors operating independently. This article reviews the technical performance and capabilities of sensors applicable to autonomous vehicles, mainly focusing on vision cameras, LiDAR and Radar sensors. The review also considers the compatibility of sensors with various software systems enabling the multi-sensor fusion approach for obstacle detection. This review article concludes by highlighting some of the challenges and possible future research directions.

Energy efficient multi-hop routing protocol for smart vehicle monitoring using intelligent sensor networks

“…For each nodal value, the proposed method consumes less amount of power when compared to existing method. 25 For example, if 5000 sensor nodes are present, then amount of power that is consumed by proposed method is 95 W, whereas for same number of nodes, existing method consumes 177 W which is much higher and amount of power is being wasted since more number of nodes remains in ideal condition.…”

“…But, the approach proposed in Haseeb et al 23 and Chaabani et al 24 is related to Lee and Kao 18 as the proposed method will use top-down approach for minimizing the energy consumption and path diversity. Even recent literatures 25,26 have also been analysed for understanding different ways of installing multiple sensors using same hardware platform. Yeong et al 25 have conducted experimental analysis using sensor fusion method under full automation process.…”

“…Even recent literatures 25,26 have also been analysed for understanding different ways of installing multiple sensors using same hardware platform. Yeong et al 25 have conducted experimental analysis using sensor fusion method under full automation process. In addition, the methods of improved reconstruction have also been examined as a survey model.…”

“…Figure 3 shows the comparison of network reliability with existing method, 25 but in previous case, the investigators have examined the same model using less number of sensors. From Figure 3, it can be seen that number of nodes has been considered from 1000 to 10,000, and reliability has been checked for every day for a period of gap between 8 and 9 h. After proper surveillance, it can be seen that the proposed method provides higher reliability when compared to existing method.…”

Acceptability Evaluation of Inter-driver Interaction via a Vehicle Agent Using Vehicle-to-Vehicle Communication on a Driving Simulator

“…Verroios et al proposed a method for limiting the range of communication by utilizing a motion sensor and a GPS [4]. Moreover, techniques have been proposed to identify which vehicle communicates with one's vehicle by combining the vehicle position from V2V with the relative position and features from sensors such as an image sensor and a radar [18]. For example, a cooperative adaptive cruise control system determines and establishes communication with the front vehicle by combining positions from V2V with radar information [19].…”