Autonomous vehicles are expected to reduce traffic accident casualties, as driver distraction accounts for 90% of accidents. These vehicles rely on sensors and controllers to operate independently, requiring robust security mechanisms to prevent malicious takeovers. This research proposes a novel approach to assessing the impact of cyber-attacks on autonomous vehicles and their surroundings, with a strong focus on prioritizing human safety. The system evaluates the severity of incidents caused by attacks, distinguishing between different events—for example, a pedestrian injury is classified as more critical than a collision with an inanimate object. By integrating deep neural network technology with feature engineering, the proposed system provides a comprehensive impact assessment. It is validated using metrics such as MAE, loss function, and Spearman’s correlation through experiments on a dataset of 5410 samples. Beyond enhancing autonomous vehicle security, this research contributes to real-world attack impact assessment, ensuring human safety remains a priority in the evolving autonomous landscape.
The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localizatio…
To address the limitation of 2D lane detection methods with monocular vision, which fail to capture the three-dimensional position of lane boundaries, this study proposes a convolutional neural network architecture for 3D lane detection. The deep residual network ResNet50 is empl…
Deep reinforcement learning (DRL) trains agents to make decisions by learning from rewards and penalties, using trial and error. It combines reinforcement learning (RL) with deep neural networks (DNNs), enabling agents to process large datasets and learn from complex environments…
Due to developments in vehicle engineering and communication technologies, vehicular networks have become an attractive and feasible solution for the future of electric, autonomous, and connected vehicles. Electric autonomous vehicles will require more data, computing resources,…
Intersection scenarios are one of the most complex and high-risk traffic scenarios. Therefore, it is important to propose a vehicle driving decision algorithm for intersection scenarios. Most of the related studies have focused on considering explicit collision risks while lackin…
Driving at an unsignalized roundabout is a complex traffic scenario that requires both traffic safety and efficiency. At the unsignalized roundabout, the driving policy does not simply maintain a safe distance for all vehicles. Instead, it pays more attention to vehicles that pot…