From 30c37feb155b19ab3bd4cc0b16154838201c89f5 Mon Sep 17 00:00:00 2001 From: dongdongO Date: Thu, 30 Nov 2023 02:40:34 +0100 Subject: [PATCH] update README --- dl-with-carla/README.md | 31 ++++++++++++++++++------------- images/lstmmodel.png | Bin 0 -> 440455 bytes 2 files changed, 18 insertions(+), 13 deletions(-) create mode 100644 images/lstmmodel.png diff --git a/dl-with-carla/README.md b/dl-with-carla/README.md index 868eeea..f2a4111 100644 --- a/dl-with-carla/README.md +++ b/dl-with-carla/README.md @@ -1,17 +1,22 @@ # Key concepts -- To obtain driving data, we used Carla, an open-source simulator. -- We employed two main methods to acquire driving data from Carla: - 1. Automatic Control - - Using the autonomous driving code implemented by Carla, we save five types of data while the vehicle is in motion: speed, steering, throttle, brake, and lane invasion. These data types are stored according to the VSS standard. Depending on the need, this data can be formatted into CSV or input into a virtual CAN. - - 2. Manual Control - - You can manually drive according to Carla's physics engine, although it is quite challenging. Like in automatic control, the same five data types are saved. - -- LSTM Learning Model +## 1. Make data for model + +To obtain driving data, we used Carla, an open-source simulator. We employed two main methods to acquire driving data from Carla: + +- Automatic Control + + Using the autonomous driving code implemented by Carla, we save five types of data while the vehicle is in motion: speed, steering, throttle, brake, and lane invasion. These data types are stored according to the VSS standard. Depending on the need, this data can be formatted into CSV or input into a virtual CAN. + +- Manual Control - We utilized an LSTM model to calculate driving scores. We chose this model because of its strength in handling time-series data, which is crucial since our CAN data is time-dependent. For more details on the model, please refer to lstm-training.py. + You can manually drive according to Carla's physics engine, although it is quite challenging. Like in automatic control, the same five data types are saved. - When the five types of data form a sequence of 100, a corresponding score is assigned. With 300,000 CAN data points, a total of 300 data sequences have been trained. Although it's not a vast amount of data, it should be sufficient to represent your driving score! + +## 2. LSTM Learning Model + +![lstmmodel.png](./images/lstmmodel.png) + +We utilized an LSTM model to calculate driving scores. We chose this model because of its strength in handling time-series data, which is crucial since our CAN data is time-dependent. For more details on the model, please refer to lstm-training.py. + +When the five types of data form a sequence of 100, a corresponding score is assigned. With 300,000 CAN data points, a total of 300 data sequences have been trained. 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