Multi-Sensor Fusion Strategies for Robust Autonomous Driving Perception under Adverse Weather and Complex Urban Traffic Conditions
Since the technology of autonomous driving is gaining momentum in its implementation in real-life conditions with multifaceted and complicated road conditions, the weaknesses of single sensors in the context of sensing accuracy, stability, and adaptability to the environment become more evident. To improve the robustness and security of autonomous driving systems in compound environments, in this paper, the research on the multi-sensor fusion technology is put into the limelight and the value of such technology applied in autonomous driving perception systems are evaluated. Thereafter, a comparison of the perception properties, benefits, and deficits of cameras and lidar is made systematically and at the data level, the fundamental patterns of multi-sensor integration are divided into three levels, namely the feature-level, sensor-level and decision-level. This study shows that when multi-sensor fusion strategies are rationally designed, the constraints of a single sensor used to sample the environment can be addressed, and this strategy plays an important role in increasing the resilience of the system as well as its ability to understand its surrounding. The discussion made in this paper offers a useful source of information when it comes to the design and implementation of multi-sensors fusion systems within the engineering field.