Multi Sensor Data Fusion and Object Tracking using Matlab Simulink

Title: - Multi Sensor Fusion and Segmentation for Multi Object Tracking using DQN in Self Driving Car under Harsh Weather Condition
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Implementation Plan:
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Step 1: Initially we load the KITTI dataset.

Step 2: Load the test image and perform Noise removal to remove these noises form the respective images using the Improved Adaptive Extended Kalman Filter(IAEKF) approach.

Step 3: Next, the contrast of the images is enhanced using Normalized Gamma Transformation based CLAHE (NGT-CLAHE) and also calculate the threshold value and adjust the missing gap regions and small regions using Improved Adaptive Weighted Mean Filter (IAWMF) approach.

Step 4: Next, To detect static infrastructure such as road, sidewalk, curbs, lane marking and buildings, but also to detect
traffic participants , perform three types of segmentation approach based on the panoptic segmentation using LIGHT G Net approach.

Step 5: Next, perform the image fusion using the Dense Net (D Net).

Step 6: Next, compute the online grid map by merging the offline and online maps using the Energy Valley Optimizer (EVO) method and also perform the path selection using using YOLO V7 model.

step 7: Next, multi objects are tracked based on position, velocity, colour, texture, shape and location using Deep Q Network (DQN) approach. And also provides extra services for enhancing self-driving car.

Step 8: Finally, The performance evaluation of this research is performed by considering several metrics which are listed as follows,
No of vehicles Vs accuracy, Velocity Vs distance, Accuracy rate Vs iteration times, Success rate Vs speed, Success ratio Vs threshold and Mean squared error Vs time.

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Software Requirement:
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1. Tool: Matlab R2020a
2. OS: Windows 10 – (64bit)
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we perform the existing process based on the title:- CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentation

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