Purdue University Graduate School
Thesis_AP_Final_V4.pdf (1.92 MB)

Crash Prediction and Collision Avoidance using Hidden Markov Model

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posted on 2019-10-16, 16:18 authored by Avinash PrabuAvinash Prabu
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.


Degree Type

  • Master of Science


  • Electrical and Computer Engineering

Campus location

  • Indianapolis

Advisor/Supervisor/Committee Chair

Lingxi Li

Additional Committee Member 2

Yaobin Chen

Additional Committee Member 3

Brian King