Мусакин, малый отель, с чего начать, как преуспеть?

Мусакин, малый отель, с чего начать, как преуспеть? - коротко

Начните с анализа целевой аудитории и создания уникального предложения, затем уделите внимание качеству сервиса и маркетингу для привлечения гостей.

Мусакин, малый отель, с чего начать, как преуспеть? - развернуто

Чтобы успешно запустить и развить малый отель, необходимо начать с тщательного анализа рынка и определения целевой аудитории. Изучите потребности потенциальных гостей, их предпочтения и бюджет. Это поможет вам сформировать уникальное предложение, которое будет выделяться среди конкурентов. Важно учитывать особенности локации: если отель находится в туристическом месте, акцент можно сделать на комфорте и удобстве; если в деловом районе — на функциональности и доступности.

Создание качественного сервиса — основа успеха. Уделите внимание деталям: чистота номеров, удобство мебели, скорость обслуживания. Персонал должен быть обучен, вежлив и готов решать любые вопросы гостей. Важно создать атмосферу гостеприимства, чтобы посетители чувствовали себя комфортно и хотели вернуться.

Маркетинг играет значительную роль в привлечении клиентов. Используйте современные инструменты: создайте сайт с удобной системой бронирования, активно работайте в социаль# Path Planning

Path Planning Project

Udacity Self-Driving Car Engineer Nanodegree Program

The goal of this project is to safely navigate a car around a virtual highway with other traffic that is driving in the same direction. The car's localization and sensor fusion data is provided by the simulator, and there is also a sparse map list of waypoints around the highway. The car should try to go as close as possible to the 50 MPH speed limit, which means passing slower traffic when possible. The car must not collide with other cars, and it must also try to stay in its lane, unless it is changing lanes. Additionally, the car must not experience total acceleration over 10 m/s^2 and jerk that is greater than 10 m/s^3.

Reflection

The path planning algorithm can be broken into three parts: prediction, behavior planning, and trajectory generation.

Prediction

The prediction module estimates what other cars will do in the future. The sensor fusion data provides information about all other cars on the same side of the road. For each car, the data includes its unique ID, its x and y position in map coordinates, its velocity in the x and y direction, and its s and d position in frenet coordinates.

Using the s and d position and the velocity in the x and y direction, we can calculate the speed of the car and predict its future s position. For each car in the same lane as our car, we can predict whether it will be in front of our car and how far away it will be in the future.

Behavior Planning

The behavior planning module decides what our car should do next. The possible behaviors are to keep lane, change lane left, or change lane right. The behavior planning module uses the predictions from the prediction module to decide which behavior is best.

The behavior planning module first checks if there is a car in front of our car. If there is no car in front of our car, the behavior planning module decides to keep lane and increases the speed of our car until it reaches the speed limit.

If there is a car in front of our car, the behavior planning module checks if it is safe to change lanes. If it is safe to change lanes, the behavior planning module decides to change lanes. If it is not safe to change lanes, the behavior planning module decides to keep lane and decreases the speed of our car until it is safe to change lanes.

Trajectory Generation

The trajectory generation module generates a trajectory for our car to follow. The trajectory generation module uses the behavior planning module's decision to generate a trajectory that smoothly transitions from our car's current state to the desired state.

The trajectory generation module uses the spline library to generate a smooth trajectory. The trajectory generation module first generates a set of waypoints that our car will follow. The waypoints are generated using the current position of our car, the desired position of our car, and the desired speed of our car. The trajectory generation module then uses the spline library to generate a smooth trajectory that our car will follow.

Results

The path planning algorithm was able to successfully navigate the car around the virtual highway with other traffic. The car was able to maintain a speed close to the speed limit, pass slower traffic when possible, and avoid collisions with other cars. The car was also able to stay in its lane unless it was changing lanes, and it did not experience total acceleration over 10 m/s^2 or jerk that was greater than 10 m/s^3.

Here is a video of the car driving around the virtual highway:

Basic Build Instructions

Clone this repo.
Make a build directory: mkdir build && cd build
Compile: cmake .. && make
Run it: ./path_planning.

Here is the data provided from the Simulator to the C++ Program

Main car's localization Data (No Noise)

["x"] The car's x position in map coordinates

["y"] The car's y position in map coordinates

["s"] The car's s position in frenet coordinates

["d"] The car's d position in frenet coordinates

["yaw"] The car's yaw angle in the map

["speed"] The car's speed in MPH

Previous path data given to the Planner

//Note: Return the previous list but with processed points removed, can be a nice tool to show how far along the path has processed since last time.

["previous_path_x"] The previous list of x points previously given to the simulator

["previous_path_y"] The previous list of y points previously given to the simulator

Previous path's end point and yaw angle

["end_path_s"] The previous list's last point's frenet s value

["end_path_d"] The previous list's last point's frenet d value

["end_path_yaw"] The previous list's last point's yaw angle

Sensor Fusion Data, a list of all other car's attributes on the same side of the road. (No Noise)

["0"] Unique car ID

["1"] Car's x position in map coordinates

["2"] Car's y position in map coordinates

["3"] Car's x velocity in m/s

["4"] Car's y velocity in m/s

["5"] Car's s position in frenet coordinates

["6"] Car's d position in frenet coordinates

Details

The car uses a perfect controller and will visit every (x,y) point it recieves in the list every .02 seconds. The units for the (x,y) points are in meters and the spacing of the points determines the speed of the car. The vector going from a point to the next point in the list dictates the angle of the car. Acceleration both in the tangential and normal directions is measured along with the jerk, the rate of change of total Acceleration. The (x,y) point path that the car recieves should work to smooth these values.
There will be some latency between the simulator running and the path planner running. The planner should be able to handle this latency and still drive the car smoothly. The previous path points are helpful for this.
The planner will output a list of (x,y) points. The simulator will visit these points every 0.02 seconds. The simulator will also provide the planner with the car's current position, yaw angle, and speed.
The planner will use the map waypoints to generate a smooth trajectory for the car to follow. The map waypoints are in the data/highway_map.csv file. The map waypoints are in the format x,y,s,dx,dy. The s value is the distance along the road to get to that waypoint in meters, the x and y values are the global map coordinates of that waypoint, and the dx and dy values are the normal vectors to the waypoint.
The simulator will provide the planner with the car's current position, yaw angle, and speed. The planner will use this information to generate a smooth trajectory for the car to follow.
The simulator will also provide the planner with a list of all other cars on the same side of the road. The planner will use this information to avoid collisions with other cars.
The planner will output a list of (x,y) points. The simulator will visit these points every 0.02 seconds. The simulator will also provide the planner with the car's current position, yaw angle, and speed.

Dependencies

cmake >= 3.5
- All OSes: click here for installation instructions
make >= 4.1
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - [install Xcode command line tools]((https://developer.apple.com/xcode/features/)
- Windows: recommend using MinGW
uWebSockets
- Run either install-mac.sh or install-ubuntu.sh.
- If you install from source, checkout to commit e94b6e1, i.e.
```
git clone https://github.com/uWebSockets/uWebSockets 
cd uWebSockets
git checkout e94b6e1
```

Editor Settings

We've purposefully kept editor configuration files out of this repo in order to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

indent using spaces
set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to ensure that students don't feel pressured to use one IDE or another.

However! I'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE that you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:

/ide_profiles/vscode/.vscode
/ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it, and how to install it.

Frankly, I've never been involved in a project with multiple IDE profiles before. I believe the best way to handle this would be to keep them out of the repo root to avoid clutter. My expectation is that most profiles will include instructions to copy files to a new location to get picked up by the IDE, but that's just me.

Hints!

You don't have to follow this directory structure, but if you do, your work will span all of the .cpp files here. Keep an eye out for TODOs.

Call for Feedback

We know this project is not perfect. If you have any feedback, please let us know.

Credits

Thank you to Udacity for providing the starter code and the simulator.

License

This project is licensed under the terms of the MIT license.