Traffic capacity implications of automated vehicles mixed with regular vehicles

Recent advances in automotive technology, such as, sensing and onboard computation, have resulted in the development of adaptive cruise control (ACC) algorithms that improve both comfort and safety. With a view towards developing advanced controllers for ACC, this paper presents an experimental platform for validation and demonstration of an online optimization based controller. Going beyond traditional PID based controllers for ACC that lack proof of safety, we construct a control framework that gives formal guarantees of correctness. In particular, safety constraints—maintaining a valid following distance from a lead car—are represented by control barrier functions (CBFs), and control objectives— achieving a desired speed—are encoded through control Lyapunov functions (CLFs). These different objectives can be unified through a quadtraic program (QP), with constraints dictated by CBFs and CLFs, that balances safety and the control objectives in an optimal fashion. This methodology is demonstrated on scale-model cars, for which the CBF-CLF based controller is implemented online, with the end result being the experimental validation of an advanced adaptive cruise controller.

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Adaptive cruise control: Experimental validation of advanced controllers on scale-model cars

A smart city is the global development vision to integrate multiple information and communication technology (ICT) solutions in a secure and simple way to manage a city's assets. Smart transportation is the basic component in future smart cities. Thus we are concentrating on the street light automation by detection of motion of object. Lighting which is not necessary can be neglected by targeted dimming of area of the city, town, roads or individual luminaries. The main objective of street light automation is to reduce the power consumption occurring due to lightening of street light during night when there is no objective motion on the street. During day time, the lights will be turned off and during night it will be turned on. In street light automation we are using Pulse Width Modulation (PWM) technique to control the light intensity in different condition like day, night, no vehicle condition & vehicle condition. In this paper we are working on street light automation by means of self responsive cars with collision avoidance system.

Effective street light automation by self responsive cars for smart transportation

After safe driving, eco-driving has become an economic and green driving idea, which is adapted to the modern engine technology and typically consists of changing a person's driving behavior based on general advice to drivers, such as accelerating slowly, driving smoothly, etc. The concept of eco-driving can help save energy and reduce pollution emissions in an efficient way. In this article, a comprehensive preliminary overview of state-of-art research on this issue is presented in several parts: methods for emission estimation, traffic signal control, cruise control, and eco-driving assist systems. Besides, the challenges and opportunities associated with eco-driving are discussed as well.

/pdf/eco-driving-current-strategies-and-issues-a-preliminary-25of6ek1ys.pdf

Eco-Driving --- Current Strategies and issues, A Preliminary Survey

Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption. Although there have been many attempts to model stop and go maneuver via traffic models, but predicting the future vehicle's acceleration in steps ahead has not been studied much in this models. The main contribution of this paper is in designing integrated genetic algorithm-artificial neural network (GA-ANN) which is a soft computing method to simulate and predict the future acceleration of the stop and go maneuver for different steps ahead based on US federal highway administration’s NGSIM dataset in real traffic flow. The results of this study are compared with two methods, back propagation based artificial neural network model (BP-ANN) and standard time series forecasting approach called ARX model. The mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination or R-squared (R2) are utilized as three criteria for evaluating predictions accuracy. The results showed the effectiveness of the proposed approach for prediction of driving acceleration time series. The proposed model can be employed in intelligent transportation systems (ITS), collision prevention systems (CPS) and driver assistant systems (DAS) such as adaptive cruise control (ACC) and etc. The outcomes of this study can be used for the automotive industries who have been seeking accurate and inexpensive tools capable of predicting vehicle speeds up to a given point ahead of time, known as prediction horizon, which can be used for designing efficient predictive controllers based on human behaviors.

Prediction of driver’s accelerating behavior in the stop and go maneuvers using genetic algorithm-artificial neural network hybrid intelligence

—Controlled & safe cruise control operation is one of the major constraints faced by today's automotive industry. Adaptive cruise control (ACC) with Stop & Go scenarios is the most important research topics in the field of intelligent transportation systems. This paper deals with the development of an ACC controller supporting stop & go function. The main feature of this controller represents adaptation to a user-preset speed and, if necessary, speed reductions can be done to maintain a safe distance from the lead vehicle in the same lane on the road, irrespective of whatever speed it is moving. The extreme case is the stop and go operation in which the lead vehicle stops and the vehicle at the rear must also do so. The main concern of the system is about the inter-distance control in highways where the vehicle velocity mainly remains constant & it also deals with the vehicle circulating in towns with frequent stops & acceleration. In both situations, goals of safety and comfort are to be achieved. The main aim of the paper is to design a model for adaptive Cruise Control system which works efficiently under the STOP & Go scenarios using Matlab and validate it with physical design architecture incorporating safety features. The paper also deals with various lane changing strategies to enforce an overall control of the vehicle.

Cruise Control Operation from Zero to Preset Speed-Simulation and Implementation

Adaptive cruise control (ACC) with Stop & Go scenarios is presently one of the most important research topics in the field of intelligent transportation systems. The main feature of this controller is that there is an adaptation to a user-present speed and, if necessary, speed reductions to keep a safe distance from the vehicle ahead in the same lane on the road, whatever the speed. The extreme case is the stop and go operation in which the lead car stops and the vehicle at the rear must also do so. The main concern of the system is about the inter-distance control in highways where the vehicle velocity mainly remains constant & it also deals with the vehicle circulating in towns with frequent stops & acceleration. In both situations, goals of safety and comfort are to be achieved. The main aim of the project is to design model based approach for adaptive Cruise Control system which works efficiently under the STOP & Go scenarios along with a basic vehicle model. The normal ACC doesn't work under the speed below 30 to 50 kmph thus causing driver inconvenience to drive. To overcome this ACC with Stop & Go is designed to work even at zero speed.

R. ArvindRaj

Papers

Cruise Control Operation from Zero to Preset Speed-Simulation and Implementation

Cruise control technique at zero speed using Matlab