Controlled Autonomous Driving for a JetRacer - Versionsgeschichte

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:42:35Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:42 Uhr
Zeile 187:		Zeile 187:
	= Summary and Outlook =		= Summary and Outlook =
	*'''Summary'''		*'''Summary'''
	:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. It also includes the Jetson Nano JetPack OS and the python server files that were used for the Jetracer side operations. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer.		:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. It also includes the Jetson Nano JetPack OS and the python server files that were used for the Jetracer side operations. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer. Vid. 1 shows the Jetracer following the right lane on the track.
	*'''Outlook'''		*'''Outlook'''
	:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.		:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Repository */

2026-07-06T12:40:49Z

Repository

← Nächstältere Version		Version vom 6. Juli 2026, 12:40 Uhr
Zeile 195:		Zeile 195:
	* Source Code should match the coding guidelines ([[Medium:Programmierrichtlinien_für_Matlab.pdf\|MATLAB<sup>®</sup>]] \| [[Medium:Programmierrichtlinie.pdf\|C]])		* Source Code should match the coding guidelines ([[Medium:Programmierrichtlinien_für_Matlab.pdf\|MATLAB<sup>®</sup>]] \| [[Medium:Programmierrichtlinie.pdf\|C]])
	* Add a header and help to your files to make them understandable for the team ([[Header_Beispiel_für_MATLAB\|MATLAB<sup>®</sup>]] \| [[Header Beispiel für C\|C]]).		* Add a header and help to your files to make them understandable for the team ([[Header_Beispiel_für_MATLAB\|MATLAB<sup>®</sup>]] \| [[Header Beispiel für C\|C]]).
	* Describe what source code does what.		* Describe what source code does what: Tab. 4 shows the usage and requirements of the source codes.
	* What are the preconditions in hardware and software (Autonomous Driving Toolbox, Parallel Computing Toolbox, etc.)?		* What are the preconditions in hardware and software (Autonomous Driving Toolbox, Parallel Computing Toolbox, etc.)?
	* Wird eine spezielle Hardware/GPU benötigt?		* Wird eine spezielle Hardware/GPU benötigt?
	{\| class="wikitable"		{\| class="wikitable"
	\|+ Source Code Usage Guideline		\|+ Tab. 4 Source Code Usage Guideline
	\|-		\|-
	! File Name !! Usage !! Software Requirements !! Hardware Requirements		! File Name !! Usage !! Software Requirements !! Hardware Requirements

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:38:09Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:38 Uhr
Zeile 187:		Zeile 187:
	= Summary and Outlook =		= Summary and Outlook =
	*'''Summary'''		*'''Summary'''
	:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. It also includes the Jetson ~~nano~~ JetPack OS and the python server files that were used for the Jetracer side operations. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer.		:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. It also includes the Jetson Nano JetPack OS and the python server files that were used for the Jetracer side operations. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer.
	*'''Outlook'''		*'''Outlook'''
	:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.		:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:37:45Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:37 Uhr
Zeile 187:		Zeile 187:
	= Summary and Outlook =		= Summary and Outlook =
	*'''Summary'''		*'''Summary'''
	:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer.		:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. It also includes the Jetson nano JetPack OS and the python server files that were used for the Jetracer side operations. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer.
	*'''Outlook'''		*'''Outlook'''
	:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.		:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:36:16Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:36 Uhr
Zeile 187:		Zeile 187:
	= Summary and Outlook =		= Summary and Outlook =
	*'''Summary'''		*'''Summary'''
	:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can b used to get a quick overview for the autonomous driving of the Jetracer.		:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can be used to get a quick overview for the autonomous driving of the Jetracer.
	*'''Outlook'''		*'''Outlook'''
	:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.		:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:35:58Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:35 Uhr
Zeile 187:		Zeile 187:
	= Summary and Outlook =		= Summary and Outlook =
	*'''Summary'''		*'''Summary'''
	:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping.		:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping. Various simulators were created during the implementation phase that can be used for lane detection simulations and PID Controller tuning. These simulators can b used to get a quick overview for the autonomous driving of the Jetracer.
	*'''Outlook'''		*'''Outlook'''
	:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.		:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:33:27Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:33 Uhr
Zeile 187:		Zeile 187:
	= Summary and Outlook =		= Summary and Outlook =
	*'''Summary'''		*'''Summary'''
	:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm , birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping.		:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm with sliding windows, birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping.

	*'''Outlook'''		*'''Outlook'''
	:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.		:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.The implementations and source codes can be used as a foundation for further development in the Autonomous driving field.

	= Repository =		= Repository =

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Implementation and Results */

2026-07-06T12:30:50Z

Implementation and Results

← Nächstältere Version		Version vom 6. Juli 2026, 12:30 Uhr
Zeile 177:		Zeile 177:

	*''' Tracking the Jetracer on Digital Map'''		*''' Tracking the Jetracer on Digital Map'''
			:The Fig. 12 shows a digital map of the track located in the autonomous system laboratory. The track lines are black in color. The red tracked path is the vehicle driving path tracked by the Topcon total station by using the prism. It shows that the car successfully drove for one complete lap in the right lane. The blue tracked path is based on the data from the gyroscope and steering throttle commands by the controller. The yaw rate data from the gyroscope is integrated to get total accumulated yaw of the vehicle. This accumulated yaw gives us the direction of the vehicle. The accumulated yaw is the multiplied by the speed commands of the controller to get the displacement of the vehicle. This displacement of the vehicle based on gyroscope and speed is the blue tracked path curve. The blue curve appears slightly rotated as compared to the lane. This is because the gyroscope based yaw rate data does not contain the lane direction information.
	[[Datei:Digital map.png\|thumb\|700px\|\|center\| Fig. 12 Digital map with Jetracer tracking]]		[[Datei:Digital map.png\|thumb\|700px\|\|center\| Fig. 12 Digital map with Jetracer tracking]]
	:The Fig. 12 shows a digital map of the track located in the autonomous system laboratory. The track lines are black in color. The red tracked path is the vehicle driving path tracked by the Topcon total station by using the prism. It shows that the car successfully drove for one complete lap in the right lane. The blue tracked path is based on the data from the gyroscope and steering throttle commands by the controller. The yaw rate data from the gyroscope is integrated to get total accumulated yaw of the vehicle. This accumulated yaw gives us the direction of the vehicle. The accumulated yaw is the multiplied by the speed commands of the controller to get the displacement of the vehicle. This displacement of the vehicle based on gyroscope and speed is the blue tracked path curve. The blue curve appears slightly rotated as compared to the lane. This is because the gyroscope based yaw rate data does not contain the lane direction information.

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Implementation and Results */

2026-07-06T12:30:25Z

Implementation and Results

← Nächstältere Version		Version vom 6. Juli 2026, 12:30 Uhr
Zeile 177:		Zeile 177:

	*''' Tracking the Jetracer on Digital Map'''		*''' Tracking the Jetracer on Digital Map'''
	[[Datei:Digital map.png\|thumb\|700px\| Fig. 12 Digital map with Jetracer tracking]]		[[Datei:Digital map.png\|thumb\|700px\|\|center\| Fig. 12 Digital map with Jetracer tracking]]
	:The Fig. 12 shows a digital map of the track located in the autonomous system laboratory. The track lines are black in color. The red tracked path is the vehicle driving path tracked by the Topcon total station by using the prism. It shows that the car successfully drove for one complete lap in the right lane. The blue tracked path is based on the data from the gyroscope and steering throttle commands by the controller. The yaw rate data from the gyroscope is integrated to get total accumulated yaw of the vehicle. This accumulated yaw gives us the direction of the vehicle. The accumulated yaw is the multiplied by the speed commands of the controller to get the displacement of the vehicle. This displacement of the vehicle based on gyroscope and speed is the blue tracked path curve. The blue curve appears slightly rotated as compared to the lane. This is because the gyroscope based yaw rate data does not contain the lane direction information.		:The Fig. 12 shows a digital map of the track located in the autonomous system laboratory. The track lines are black in color. The red tracked path is the vehicle driving path tracked by the Topcon total station by using the prism. It shows that the car successfully drove for one complete lap in the right lane. The blue tracked path is based on the data from the gyroscope and steering throttle commands by the controller. The yaw rate data from the gyroscope is integrated to get total accumulated yaw of the vehicle. This accumulated yaw gives us the direction of the vehicle. The accumulated yaw is the multiplied by the speed commands of the controller to get the displacement of the vehicle. This displacement of the vehicle based on gyroscope and speed is the blue tracked path curve. The blue curve appears slightly rotated as compared to the lane. This is because the gyroscope based yaw rate data does not contain the lane direction information.

Zeile 184:		Zeile 184:
	*''' Accumulated Yaw comparison of Jetracer'''		*''' Accumulated Yaw comparison of Jetracer'''
	:The accumulated yaw rate data collected by the gyroscope is integrated over time change using trapezoid rule to get the accumulated total yaw of the car. The Topcon based vehicle displacement ground truth data is differentiated over time to get the yaw of the vehicle. The Fig. 12 provides the illustration for this analysis. The red curve represents the Topcon ground truth based accumulated yaw data while the blue curve represents the Jetracer gyroscope based accumulated yaw data. The vehicle starts moving from time equal to zero seconds hence this should be the starting point for the observation. It can be observed that the curves of accumulated yaw from both the Topcon prism tracking and the Jetracer follow very closely to one another. This points to a successful gyroscope implementation. A drift in the gyroscope accumulate yaw curve is observed over a significant amount of time. This is cause by the mathematical properties of integration which is used to calculate the accumulated yaw data from the raw yaw rate data output of the gyroscope.		:The accumulated yaw rate data collected by the gyroscope is integrated over time change using trapezoid rule to get the accumulated total yaw of the car. The Topcon based vehicle displacement ground truth data is differentiated over time to get the yaw of the vehicle. The Fig. 12 provides the illustration for this analysis. The red curve represents the Topcon ground truth based accumulated yaw data while the blue curve represents the Jetracer gyroscope based accumulated yaw data. The vehicle starts moving from time equal to zero seconds hence this should be the starting point for the observation. It can be observed that the curves of accumulated yaw from both the Topcon prism tracking and the Jetracer follow very closely to one another. This points to a successful gyroscope implementation. A drift in the gyroscope accumulate yaw curve is observed over a significant amount of time. This is cause by the mathematical properties of integration which is used to calculate the accumulated yaw data from the raw yaw rate data output of the gyroscope.
	[[Datei:Accumulated yaw.png\|thumb\|1200px\|~~left~~ \| Fig. 12 Accumulated yaw gyroscope vs Tocpon prisma]]		[[Datei:Accumulated yaw.png\|thumb\|1200px\|center\| Fig. 12 Accumulated yaw gyroscope vs Tocpon prisma]]

	= Summary and Outlook =		= Summary and Outlook =

Yashodhan-vishvesh.deshpande@stud.hshl.de: /* Summary and Outlook */

2026-07-06T12:29:44Z

Summary and Outlook

← Nächstältere Version		Version vom 6. Juli 2026, 12:29 Uhr
Zeile 187:		Zeile 187:

	= Summary and Outlook =		= Summary and Outlook =
			*'''Summary'''
			:All the tasks of this bachelor thesis of steering the autonomous vehicle in the right lane were achieved. The thesis uses classical lane tracking algorithm , birds eye view transform , lane detection implementation, steering using PID controller, gyroscope yaw rate data measurement, LIDAR module implementation for obstacle avoidance and Topcon prism tracking based ground truth data collection. This thesis showcases an example of academic undergraduate level autonomous lane keeping.

			*'''Outlook'''
			:The future students can compare this classical lane tracking algorithm with AI based implementations. This classical algorithm can also be used to build models to train deep learning neural networks. Also students can use various types of controllers like Model Predictive controller and compare them to PID controller. Multiple sensor fusion can be done with the Jetson Nano board to increase the capabilities of the Jetracer for better autonomous driving.

	= Repository =		= Repository =