# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 3 Related Work
### Hybrid Image Fusion Models  
[PERSON] et al. [14] have proposed a novel hybrid multi-sensor fusion pipeline configuration for autonomous cars that handles environment perception tasks such as road segmentation, obstacle identification, and tracking. A suggested encoder-decoder-based fully convolutional neural network (FCNx) and a standard extended Kalman filter (EKF) nonlinear state estimator approach are used in this fusion framework. It also employs an optimal camera, LiDAR, and radar sensor configurations for each fusion approach. The purpose of this hybrid architecture is to create a fusion system that is cost-effective, lightweight, adaptable, and resilient (in the event of a sensor failure). It employs the FCNx algorithm, which improves road identification accuracy above benchmark models while preserving real-time efficiency in an embedded computer for autonomous vehicles. [PERSON] et al. [15] have presented a hybrid spatiotemporal fusion (STF) technique based on a deep learning model called the hybrid deep-learning-based spatiotemporal fusion model (HDLSFM). With a minimum amount of input, the method develops a hybrid framework for the reliable fusion of morphological and physiological data that explains the physical material at the surface of the earth. To handle radiation discrepancies across various types of satellite pictures, the suggested method combines a regressive deep-learning-based related radiometric normalization, a deep-learning-based super-resolution, and a linear-based fusion. Using Fit-FC as a benchmark, the HDLSFM's propensity to predict phenological and land-cover change has been demonstrated. Meanwhile, HDLSFM is immune to changes in radiation across different types of satellite images as well as the time interval between the forecast and base dates, assuring its usefulness in the synthesis of fused time-series data. [PERSON] et al. [16] have proposed a hybrid fusion strategy that takes into consideration the geographical and semantic properties of sensor inputs concerning occurrences. To achieve this, the authors have used Cmage, an image-based representation for both physical and social sensor data that describes the situation of certain visual notions (e.g., \"crowdedness\" and \"people marching\"). The authors have proposed a fusion model that describes sparse sensor information using a Gaussian process based on the acquired Cmage representation, which combines multimodal event signals with a Bayesian method and integrates spatial links between the sensor and social data. [PERSON] et al. [17] have proposed a selective sensor fusion framework, namely HydraFusion, which learns to recognize the present driving environment and then combines the appropriate mix of sensors to enhance robustness without sacrificing efficiency. HydraFusion is the first method to suggest dynamically shifting between early fusion, late fusion, and combinations in between, so modifying both how and when fusion is used. On the industry-standard Nvidia Drive PX2 AV hardware platform, the authors show that HydraFusion outperforms early and late fusion techniques by 13.66 percent and 14.54 percent, respectively, without increasing computing complexity or energy consumption. Both static- and deep-learning-based context identification algorithms are proposed and evaluated by the authors. [PERSON] et al. [18] has proposed a hybrid spatial-temporal-spectral image fusion model (HSTSFM) for simultaneously generating synthetic satellite data with high spatial, temporal, and spectral resolution (STSR), which blends the high spatial resolution from the panchromatic image of the Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multispectral image of the Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high spatial-spectral image fusion, high spatial-temporal image fusion, and high temporal-spectral image fusion, which are the three fusion modules included in the proposed HSTSFM.