Project group 12 ppt.pptx Project group 12 ppt.pptx

Project group 12 ppt.pptx

1. Madan Mohan Malaviya Univ ersity of Technology, Gorakhpur Lane Change Detection Using Image Processing Based On Deep Learning * Side 1 Presented By: Siddharth Singh (2021071037) Shruti Sharma (2021071036) Adarsh Singh (2022072001) Information Technology and Computer Application Department Madan Mohan Mal aviya University of Technology , Gorakhpu r Project Supervisor : Dr. R. K. Dwivedi (Asst. Prof.)

2. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 2 Introduction Lane change detection is a crucial aspect of autonomous or semi- autonomous driving systems. According to the report of WHO, approximately 1.3 million people die from road accidents every year. As a human driver, it is hard to maintain correct lane and proper gap with the front vehicle for an extended period of time. Hence, traditional and modern methods were framed to prevent accidental lane changes while driving. Traditionally, most conventional lane detection techniques adhere to pipelines, which typically consists of image preprocessing, feature extraction, lane model fitting, line tracking. Traditional methods failed to attain their goal when encountered with improper road conditions. With introduction of AI, the limitations of traditional methods were addressed

3. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 3 Machine Learning and Deep Learning are two primary categories that may be used to classify most of the AI approaches used in lane detection. DL is more popular than ML due to its effective performance in classification, detection or utilizing image frames as input to the network algorithm. Lane detection is an integral part of Advanced Driver Assistance System (ADAS) which makes the driving environment safer for drivers. Adaptive cruise control, Automatic emergency braking, Blind Spot Assist, Lane Keep Assist, Lane Departure Warning System, Lane Detection etc. are several examples of ADAS module.

4. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 4 Early Approaches to Lane Change Detection Methodologies: Rule-based algorithms with handcrafted features like curvature, lane width, and vehicle speed. Initial attempts at applying basic machine learning models (e.g., SVM, decision trees) on feature-engineered data. Challenges: Limited adaptability to diverse driving scenarios. Difficulty in accurately capturing dynamic lane change manoeuvres. Further a shift was made, and deep learning was introduced.

5. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 5 Notable Research Contributions in the past in Lane Change Detection LaneNet (2018): LaneNet introduced a real-time lane detection network based on CNNs. It was capable of accurately detecting lane markings and predicting lane changes in real-world driving scenarios, laying the foundation for subsequent research in this area. Lane Change Detection Using Deep Learning and Long Short-Term Memory Networks (2019): This study integrated Long Short-Term Memory (LSTM) networks with CNNs to capture temporal dependencies in lane change behaviors. By incorporating LSTM units, the model could better predict lane changes by considering the sequential nature of driving maneuvers. Lane Change Detection with Spatiotemporal Deep Learning (2021): This research proposed spatiotemporal deep learning models specifically designed for lane change detection tasks. By jointly modeling spatial and temporal features, these models achieved improved accuracy and robustness in identifying lane changes under diverse driving conditions.

6. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 6 Importance of Lane Change Detection Lane changes are essential for safe driving and manoeuvring in traffic. Detecting lane changes can enhance driver assistance systems and autonomous vehicles' capabilities. ADAS incorporates the lane change detection for semi autonomous or fully autonomous vehicles. Accurate lane change detection contributes to improving overall passenger safety.

7. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 7 Challenges in Lane Change Detection Variability in road conditions and lighting can impact the accuracy of lane change detection. Distinguishing between intentional and unintentional lane changes is a challenging task. Real-time processing requirements for lane change detection demand efficient algorithms. Unmarked lane line conditions pose a serious challenge to the algorithms as the image collected cannot detect lane lines. Drastic illumination changes.

8. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 8 Image Processing in Lane Change Detection Image processing techniques are used to extract lane markings and vehicle positions from camera inputs. Deep learning models, such as Convolutional Neural Networks (CNNs), are effective in analyzing visual data for lane change detection. Preprocessing steps like image enhancement and feature extraction play a crucial role in improving detection accuracy.

9. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 9 Deep Learning for Lane Change Detection Deep learning algorithms can learn complex patterns and features from image data for lane change detection. Training deep neural networks on annotated datasets can enable the model to identify lane changes with high accuracy. Transfer learning techniques can be applied to leverage pre-trained models for lane change detection tasks.

10. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 10 Data Collection and Annotation Annotated datasets containing images of lane changes are essential for training deep learning models. Manual annotation of lane change instances in images is a time-consuming process. Automated annotation tools and crowd-sourced labeling can expedite the dataset creation process.

11. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 11 Model Training and Evaluation Splitting the dataset into training and validation sets is crucial for training deep learning models. Metrics like accuracy, precision, recall, and F1 score are used to evaluate the performance of lane change detection models. Fine-tuning hyperparameters and model architecture optimization can improve detection accuracy.

12. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 12 Real-Time Implementation Implementing deep learning models for lane change detection in real-time requires efficient hardware and software integration. Optimizing inference speed and latency is essential for deploying the system in real-world scenarios. Integration with onboard sensors and actuators enables autonomous vehicles to react to detected lane changes.

13. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 13 Algorithms Implemented 1. Convolutional Neural Networks (CNNs): CNNs are commonly used for lane detection tasks due to their effectiveness in feature extraction from images. Fully Convolutional Networks (FCNs) are particularly popular for pixel-wise segmentation tasks like lane detection. Variants like U-Net, SegNet , and DeepLab have been adapted for lane detection tasks, providing high-resolution output while capturing contextual information . 2. Encoder-Decoder Architectures: Encoder-decoder architectures are widely used for tasks requiring spatial information preservation and feature extraction. These architectures typically consist of an encoder module to extract features and a decoder module to generate high-resolution output. They are effective for lane detection as they can capture both global and local contextual information.

14. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 14 3 . Recurrent Neural Networks (RNNs): RNNs, particularly Long Short-Term Memory (LSTM) networks, have been used for sequential data processing in lane detection. They can capture temporal dependencies and context information over time, which is beneficial for lane detection in video sequences. 5. Attention Mechanisms: Attention mechanisms enable models to focus on relevant parts of the input image while ignoring irrelevant regions. They have been used in lane detection to prioritize features in the image that are most likely to correspond to lane markings. Attention mechanisms can improve the accuracy and efficiency of lane detection models by reducing the computational burden on irrelevant areas.

15. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 15 Challenges or Limitations of model (CNN) Generalization to Diverse Conditions: Deep learning models trained on specific datasets or environments may struggle to generalize to unseen conditions, such as different lighting conditions, weather, road types, and geographical locations. Overfitting to training data can occur if the model learns spurious correlations or memorizes irrelevant features, leading to poor performance in real-world scenarios. Lane markings exhibit diverse shapes, colors , widths, and textures, making it challenging for deep learning models to accurately detect and classify them. Ambiguous or degraded lane markings, such as faded lines, occluded markings, or temporary road constructions, pose additional challenges for deep learning-based lane detection systems . Real-Time Performance and Efficiency: Lane detection systems must operate in real-time to support timely decision-making for autonomous vehicles and driver assistance systems. Deep learning models with high computational complexity may struggle to meet real-time processing requirements on embedded platforms with limited computational resources.

16. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 16 Future Developments Incorporating multi-sensor data fusion for enhanced lane change detection capabilities. Continuous model retraining and adaptation to evolving road conditions. Research on robustness and generalization of deep learning models for diverse driving scenarios.

17. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 17 Challenges and Opportunities Addressing scalability challenges in deploying lane change detection systems across different vehicle platforms. Exploring opportunities for integrating lane change detection with other intelligent transportation systems. Collaborative research and development efforts to overcome technical barriers and advance the field.

18. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 18 Conclusion Lane change detection using deep learning offers significant potential for improving driving safety and efficiency. Continued research and innovation in image processing and deep learning will drive advancements in lane change detection. Collaboration among industry, academia, and regulatory bodies is crucial for the successful adoption of lane change detection technologies.

19. Madan Mohan Malaviya Univ. of Technology, Gorakhpur * Side 19