Design of a lane departure driver-assist system under safety specifications

We use a controlled invariance approach to design a semi-autonomous lane departure assist system that is guaranteed to keep the vehicle in the lane. The controlled invariant safe set is the set of system states from which an input exists that can keep the vehicle in the lane. First we provide theoretical conditions under which the controlled invariant safe set has a simple characterization that can be quickly computed in real-time. We then use this characterization to derive a feedback strategy that keeps the vehicle in the lane and overrides the driver only if he/she could otherwise force a future lane departure. We also provide a detailed description of the above mentioned conditions, including algorithmic approaches that allow to verify whether these conditions are satisfied

Crash Prediction and Collision Avoidance using Hidden Markov Model

Indiana University-Purdue University Indianapolis (IUPUI)Automotive technology has grown from strength to strength in the recent years. The main focus of research in the near past and the immediate future are autonomous vehicles. Autonomous vehicles range from level 1 to level 5, depending on the percentage of machine intervention while driving. To make a smooth transition from human driving and machine intervention, the prediction of human driving behavior is critical. This thesis is a subset of driving behavior prediction. The objective of this thesis is to predict the possibility of crash and implement an appropriate active safety system to prevent the same. The prediction of crash requires data of transition between lanes, and speed ranges. This is achieved through a variation of hidden Markov model. With the crash prediction and analysis of the Markov models, the required ADAS system is activated. The above concept is divided into sections and an algorithm was developed. The algorithm is then scripted into MATLAB for simulation. The results of the simulation is recorded and analyzed to prove the idea

Modeling and Simulation of Lane Keeping Support System Using Hybrid Petri Nets

Indiana University-Purdue University Indianapolis (IUPUI)In the past decades, the rapid innovation of technology has greatly affected the automotive industry. However, every innovation has always been paired with safety risks that needs to be quickly addressed. This is where Petri nets (PNs) have come into the picture and have been used to model complex systems for different purposes, such as production management, traffic flow estimation and the introduction of new car features collectively known as, Adaptive Driver Assistance Systems (ADAS). Since most of these systems include both discrete and continuous dynamics, the Hybrid Petri net (HPN) model is an essential tool to model these. The objective of this thesis is to develop, analyze and simulate a lane keeping support system using an HPN model. Chapter 1 includes a brief summary of the specific ADAS used, lane departure warning and lane keeping assist systems and then related work on PNs is mentioned. Chapter 2 provides a background on Petri nets. In chapter 3, we develop a discrete PN model first, then we integrate continuous dynamics to extend it to a HPN model that combines the functionalities of the two independent ADAS systems. Several scenarios are introduced to explain the expected model behavior. Chapter 4 presents the analysis and simulation results obtained on the final model. Chapter 5 provides a summary for the work done and discusses future work