However, upon successfully completion of the final examination, you would get a certificate from the Institute of Chartered Accountants of Sri Lanka. What is the Investment for this course? Do I need to have prerequisites to enroll the course? No advance preparation is required for this course.
Our model is designed to not only predict the immediate kinematics of the vehicles in the transition phase but also the complete four-phase lane change maneuvers.
The trajectory of each remote vehicle is modeled as a time series. In our model, we separate the learning of lateral and longitudinal behaviors of the driver as they are influenced by different control inputs. Artificial neural networks ANNs are one of the most famous tools for description and prediction of nonlinear systems [ 4647 ].
Neural networks with hidden units can principally predict any well-behaved function. In the case of time series, in order to handle the dependency of the prediction to a finite set of past values and time varying nature of the input signals, neural network topologies need to be equipped with a short term memory mechanism which is called the feedback delay.
NARX is a neural network with feedback delay that can be trained and used to predict a time series from its past values and an exogenous one, in spite of NAR which does not rely on any external inputs.
We use NAR model to predict the future pattern of different system inputs, i. A NARX model is employed to predict the longitudinal trajectory of the vehicle during the lane change using some of the previously estimated sequences of input signals as the exogenous input.
The exogenous inputs in our framework are yaw rate, heading, speed, and longitudinal acceleration. Finally, an RNN is adopted to model the lateral trajectory of the vehicle based on the predicted input signals.
RNNs can use their internal memory to process arbitrary sequences of inputs. The input signals to our lateral position prediction RNN are steering wheel angle, yaw rate, and heading. Using the internal memory, the RNN can distinguish between different maneuvers with partially similar input signals.
For example, a steering due to the road curvature might look partially similar to the one from lane change maneuver, but the RNN can learn to distinguish between these two maneuvers by looking at a longer history of the signals or other input signals, such as road curvature.
In the former case, the RNN should also be trained on other maneuvers which share the same input signal patterns in a portion of their lifetime. All of the ANN models are batch trained and the training phase is offline due to the low computational cost of batch training and insufficient accessible data for online training.
In order to use the full capability of neural networks, the input signals for all ANNs are normalized to [-1, 1] range. Then, the input signals are differenced to remove the linearity and improve the nonlinearity prediction process.
The resulting time series is known as integrated time series. The value of predicted location can be reconstructed by adding the first actual value to the estimated difference in the series.
Smoothed, Normalized, and Integrated input signals of a single lane change maneuver To mitigate the effect of noise, small variations of input signals, based on the nature of the signal, are filtered to smooth the time series and mitigate the effect of noise.
Variation smaller than 3 degrees, 0. The resulting input signals during one maneuver is shown in Fig.
All of our ANNs have a hidden layer with 20 nodes and 15 step short term memory, which means that they are using the past information of 1. As mentioned before, the NAR is used to model the patterns of input signals to the system.
The longitudinal position of the vehicle is modeled based on the predicted values of heading, speed, and longitudinal acceleration as external inputs.
On the other hand, the RNN should not only predict the future lateral position of the vehicle, but should also distinguish between different lateral maneuvers. Therefore, the lateral model is also trained with some curve road data to be able to differentiate between different lateral movements.
Procedure of cut-in probability Pc calculation 3. SMPC design details are discussed in the following subsection. At each prediction cycle we have Sm predicted future values for each of the longitudinal and lateral relative positions of the suspicious cut-in vehicle.
Hence, we have Sm rectangular areas, each of them determines the predicted area for the position of the cut-in vehicle in the corresponding upcoming time step with 90 percent accuracy. We take the most conservative approach to define the cut-in probability, Pc as follows: Each of these Sm rectangles, A in Fig.
The resultant intersection area, A1is normalized by dividing it by the corresponding predicted area value, Ato calculate the probability value of being inside the bad-set for each of these predictions. The maximum value amongst these Sm probabilities is selected as the Pc value for that prediction cycle.However, with the convergence to LKAS 39, the measurement of financial instruments was diverted to fair value.
The superiority of fair value measurement over historical cost accounting has been gaining broad-based acceptance among accounting professionals and . Sri Lanka converges with IFRS. LKAS 39 Financial Instruments: Recognition and Measurement and SLFRS 7 Financial Instruments: Disclosures deal with presentation, recognition and measurement and disclosure aspects of financial instruments, in a comprehensive manner.
I and will be the convergence, ecttlo up, (by eneb old scurej and Cin'wUh 7ieS 'wallowed up: and iio1e t. of much OOCJUNOO have free aoceas to the Medical . Get 24/7 Benefits Of Achieving Convergence With Ifrs Assignment Help / Homework Help Online from experts on lausannecongress2018.com 30% discount % Cashback* + Benefits Of Achieving Convergence With Ifrs Experts.
Ask Now! Get % error-free solutions at affordable prices. Convergence with IFRSs in Sri Lanka; Conceptual framework; Fair value measurement (SLFRS13) (LKAS 34 and IFRIC 10) Discontinued operations (SLFRS 5) Assets.
Inventories (LKAS 2) Financial assets and liabilities, hedging and derivatives (LKAS 32, LKAS 39, SLFRS 9) Financial instrument disclosures (SLFRS 7) Income Taxes. The objectives of the improvements were to reduce or eliminate alternatives, redundancies and conflicts within the Standards, to deal with some convergence issues and to make other improvements.