Browsing by Subject "ADAS"

Now showing 1 - 4 of 4
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    Design and modeling of adaptive cruise control system using petri nets with fault tolerance capabilities
    (2018) Chandramohan, Nivethitha Amudha; Li, Lingxi
    In automotive industry, driver assistance and active safety features are main areas of research. This thesis concentrates on designing one of the famous ADAS system feature called Adaptive cruise control. Feature development and analysis of various functionalities involved in the system control are done using Petri Nets. A background on the past and current ACC research is noted and taken as motivation. The idea is to implement the adaptive cruise control system in Petri net and analyze how to provide fault tolerance to the system. The system can be evaluated for various cases. The ACC technology implemented in di erent cars were compared and discussed. The interaction of the ACC module with other modules in the car is explained. The cruise system's algorithm in Petri net is used as the basis for developing Adaptive Cruise Control system's algorithm. The ACC system model is designed using Petri nets and various Petri net functionalities like place invariant, transition invariant and reachability tree of the model are analyzed. The results are veri ed using Matlab. Controllers are introduced for ideal cases and are implemented in Petri nets. Then the error cases are considered and fault tolerance techniques are carried out on the model to identify the fault places.
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    Implementation of ADAS features on One-Tenth scale of an Autonomous Vehicle
    (2021-12) Davuluri, Yogitha; Li, Lingxi; Chen, Yaobin; King, Brian
    An autonomous car is a self-driving vehicle, that operates without human intervention and has the capability of sensing the environment around it. To achieve this, the autonomous vehicle mostly depends on multiple Sensors, Actuators, Machine learning, complex algorithms and processors for software execution. Developed Software, at that point, processes all the information obtained from sensors, plans the path, and the instructions are passed to the vehicle’s actuators, which are capable of controlling acceleration, steering, and brake systems. The rules that are hard-coded, algorithms for detection of object and obstacle avoidance, and predictive modelling control algorithms assist the software with observing traffic guidelines and navigate the vehicle accordingly. Free driving is anything but a simple assignment, and to make independent driving game plans is an extraordinarily critical capacity in the current programming planning field. Engineers and Researchers have been keeping huge endeavors to develop safe and precise algorithms to be incorporated in autonomous vehicles. ROS is a flexible and perfect middle ware tool for robotic applications. ROS offers the necessary tools to effortlessly get the sensors information, process that information, and produce a suitable response to actuators of the vehicle. This thesis work plans to exhibit how ROS could be utilized as a middle- ware tool to make the vehicle move autonomously by examining the surroundings and taking decision. The main focus of this thesis is to develop a one-tenth scale of an autonomous Racecar equipped with Jetson Nano as the on-board computer, ROS based software architecture, sensors, and a PWM driver and implement ADAS features such as Emergency Brake system, Lane Detection and Lane change on the autonomous Race car vehicle. At last, by following the strategies introduced in this thesis work, it is possible to build and develop an autonomousvehicle that uses ROS framework.
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    Integration of V2V-AEB system with wearable cardiac monitoring system and reduction of V2V-AEB system time constraints
    (2017) Bhatnagar, Shalabh; Chien, Stanley
    Autonomous Emergency Braking (AEB) system uses vehicle’s on-board sensors such as radar, LIDAR, camera, infrared, etc. to detect the potential collisions, alert the driver and make safety braking decision to avoid a potential collision. Its limitation is that it requires clear line-of-sight to detect what is in front of the vehicle. Whereas, in current V2V (vehicle-to-vehicle communication) systems, vehicles communicate with each other over a wireless network and share information about their states. Thus the safety of a V2V system is limited to the vehicles with communication capabilities. Our idea is to integrate the complementary capabilities of V2V and AEB systems together to overcome the limitations of V2V and AEB systems. In a V2V-AEB system, vehicles exchange data about the objects information detected by their onboard sensors along with their locations, speeds, and movements. The object information detected by a vehicle and the information received through the V2V network is processed by the AEB system of the subject vehicle. If there is an imminent crash, the AEB system alerts the driver or applies the brake automatically in critical conditions to prevent the collision. To make V2V-AEB system advance, we have developed an intelligent heart Monitoring system and integrated it with the V2V-AEB system of the vehicle. The advancement of wearable and implantable sensors enables them to communicate driver’s health conditions with PC’s and handheld devices. Part of this thesis work concentrates on monitoring the driver’s heart status in real time by using fitness tracker. In the case of a critical health condition such as the cardiac arrest of a driver, the system informs the vehicle to take an appropriate operation decision and broadcast emergency messages over the V2V network. Thus making other vehicles and emergency services aware of the emergency condition, which can help a driver to get immediate medical attention and prevent accident casualties. To ensure that the effectiveness of the V2V-AEB system is not reduced by a time delay, it is necessary to study the effect of delay thoroughly and to handle them properly. One common practice to control the delayed vehicle trajectory information is to extrapolate trajectory to the current time. We have put forward a dynamic system that can help to reduce the effect of delay in different environments without extrapolating trajectory of the pedestrian. This method dynamically controls the AEB start braking time according to the estimated delay time in the scenario. This thesis also addresses the problem of communication overload caused by V2V-AEB system. If there are n vehicles in a V2V network and each vehicle detects m objects, the message density in the V2V network will be n*m. Processing these many messages by the receiving vehicle will take considerable computation power and cause a delay in making the braking decision. To prevent flooding of messages in V2V-AEB system, some approaches are suggested to reduce the number of messages in the V2V network that include not sending information of objects that do not cause a potential collision and grouping the object information in messages.
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    Intersection Collision Avoidance For Autonomous Vehicles Using Petri Nets
    (2019-08) Shankar Kumar, Valli Sanghami; Li, Lingxi; Chen, Yaobin; King, Brian
    Autonomous vehicles currently dominate the automobile field for their impact on humanity and society. Connected and Automated Vehicles (CAV’s) are vehicles that use different communication technologies to communicate with other vehicles, infrastructure, the cloud, etc. With the information received from the sensors present, the vehicles analyze and take necessary steps for smooth, collision-free driving. This the sis talks about the cruise control system along with the intersection collision avoidance system based on Petri net models. It consists of two internal controllers for velocity and distance control, respectively, and three external ones for collision avoidance. Fault-tolerant redundant controllers are designed to keep these three controllers in check. The model is built using a PN toolbox and tested for various scenarios. The model is also validated, and its distinct properties are analyzed.