LSTM-based Preceding Vehicle Behaviour Prediction during Aggressive Lane Change for ACC Application

The development of Adaptive Cruise Control (ACC) systems aims to enhance the safety and comfort of vehicles by automatically regulating the speed of the vehicle to ensure a safe gap from the preceding vehicle. However, conventional ACC systems are unable to adapt themselves to changing driving conditions and drivers' behavior. To address this limitation, we propose a Long Short-Term Memory (LSTM) based ACC system that can learn from past driving experiences and adapt and predict new situations in real time. The model is constructed based on the real-world highD dataset, acquired from German highways with the assistance of camera-equipped drones. We evaluated the ACC system under aggressive lane changes when the side lane preceding vehicle cut off, forcing the targeted driver to reduce speed. To this end, the proposed system was assessed on a simulated driving environment and compared with a feedforward Artificial Neural Network (ANN) model and Model Predictive Control (MPC) model. The results show that the LSTM-based system is 19.25% more accurate than the ANN model and 5.9% more accurate than the MPC model in terms of predicting future values of subject vehicle acceleration. The simulation is done in Matlab/Simulink environment

Movement Optimization of Robotic Arms for Energy and Time Reduction using Evolutionary Algorithms

Trajectory optimization of a robot manipulator consists of both optimization of the robot movement as well as optimization of the robot end-effector path. This paper aims to find optimum movement parameters including movement type, speed, and acceleration to minimize robot energy. Trajectory optimization by minimizing the energy would increase the longevity of robotic manipulators. We utilized the particle swarm optimization method to find the movement parameters leading to minimum energy consumption. The effectiveness of the proposed method is demonstrated on different trajectories. Experimental results show that 49% efficiency was obtained using a UR5 robotic arm

A Novel Model for Driver Lane Change Prediction in Cooperative Adaptive Cruise Control Systems

Accurate lane change prediction can reduce potential accidents and contribute to higher road safety. Adaptive cruise control (ACC), lane departure avoidance (LDA), and lane keeping assistance (LKA) are some conventional modules in advanced driver assistance systems (ADAS). Thanks to vehicle-to-vehicle communication (V2V), vehicles can share traffic information with surrounding vehicles, enabling cooperative adaptive cruise control (CACC). While ACC relies on the vehicle's sensors to obtain the position and velocity of the leading vehicle, CACC also has access to the acceleration of multiple vehicles through V2V communication. This paper compares the type of information (position, velocity, acceleration) and the number of surrounding vehicles for driver lane change prediction. We trained an LSTM (Long Short-Term Memory) on the HighD dataset to predict lane change intention. Results indicate a significant improvement in accuracy with an increase in the number of surrounding vehicles and the information received from them. Specifically, the proposed model can predict the ego vehicle lane change with 59.15% and 92.43% accuracy in ACC and CACC scenarios, respectively

Optimized Implementation of Memristor-Based Full Adder by Material Implication Logic

Recently memristor-based applications and circuits are receiving an increased attention. Furthermore, memristors are also applied in logic circuit design. Material implication logic is one of the main areas with memristors. In this paper an optimized memristor-based full adder design by material implication logic is presented. This design needs 27 memristors and less area in comparison with typical CMOS-based 8-bit full adders. Also the presented full adder needs only 184 computational steps which enhance former full adder design speed by 20 percent.Comment: International Conference on Electronics Circuits and Systems (ICECS), 201

An Efficient Algorithm for Face Localization An Efficient Algorithm for Face Localization

Detecting and localizing a face in a single image is the most important part of almost all face recognition systems. Face localization aims to determine the image position of a face for verification purpose of documents such as passport, driving license, ID cards, etc. In this paper an entropy-based method is proposed for detecting the high information region of the image which may include eyes, mouth, nose, etc. The derived regions in this stage of recognition are sent to feature extraction and classification phase. The method has been tested on the ORL database. The results show the effectiveness and robustness of the proposed method for face detection and localization in presence of white additive Gaussian noise up to 25 dbw. We have achieved localization rate 99.75 % for detection of faces in the ORL data set that we had which means 1 miss over 400 ORL faces

Inverted Pendulum Control with a Robotic Arm using Deep Reinforcement Learning

Inverted pendulum control is a benchmark control problem that researchers have used to test the new control strategies over the past 50 years. Deep Reinforcement Learning Algorithm is used recently on the inverted pendulum on a straightforward form. The inverted pendulum had only one degree of freedom and was moving on a plane. This paper demonstrates a successful implementation of a deep reinforcement learning algorithm on an inverted pendulum that rotates freely on a spherical joint with an industrial 6 degrees freedom robot arm. This research used the Deep Reinforcement Learning algorithm in Robot Operating System (ROS) and Gazebo Simulation. Experimental results show that the proposed method achieved promising outputs and reaches the control objectives. We were able to control the inverted pendulum upward for 30 and 20 seconds in two case studies. Two other significant novelties in this research are using an inertial measurement unit (IMU) on the tip of the pendulum, that will facilitate implementation on the real robot for future work and different reward functions in comparing to past publications that enable continuous learning and mastering control in a vertical positio

Multi-Modal Signal Analysis for Underwater Acoustic Sound Processing

Acoustic sound source localization in a shallow water environment is an impactful area of research for environmental and marine-life monitoring. Most available sound source localization techniques require multiple hydrophones, which can be costly, complicated, and hard to maintain. In this paper, we utilized the modal dispersions of a signal to derive a single hydrophone-based localization method for a noisy, shallow water environment. Moreover, we investigated the effects of underwater ambient noise on the accuracy of the proposed method. Our proposed method can select multi-modals for localization to increase accuracy in low SNR environments. We evaluated our proposed method in various SNRs. Finally, we compared our results with previous works, which showed improvement

Time delay estimation in underwater environment using cross-correlation based techniques

In this paper, we analyzed the time difference of arrival (TDOA) of acoustic sounds in an underwater environment. TDOA estimation accuracy can be greatly influenced by factors such as target frequency, noise, channel models, and sampling frequency of the receiver. The underwater environment is a relatively low SNR environment and to have an accurate estimation of TDOA, noise effects on the estimator should not be neglected. To show the effects of SNR on the accuracy of estimation, we analyzed and simulated multiple TDOA based methods, in different SNR condition