Projects

Selected Doctoral Research Projects

Student Capstone Projects

Development, testing, and validation of ADAS systems in a scaled vehicle model

This project looks to explore and study ADAS controller design methods by developing, verifying and validating two ADAS controllers, namely, lane keeping and adaptive cruise control. These controllers are considered for a fully autonomous car, with the surrounding data coming from sensors such as a camera and a LIDAR. Model predictive controllers are considered for the control methodology after a detailed literature survey. The developed control system is verified and compared with those in literature, and small improvements are added to aid better state estimation and performance in different conditions. Detailed simulations are run for both high and low speed cases using a real size car modeled in a simulation environment. Additionally, verified controllers are then integrated and implemented on a 1/10th scale RC car, and validation simulations are performed. During this stage some modification to controllers is done to allow it to be implemented on a car 1/10th the size of a real car. Lastly, this project also develops a new 1/10th scale RC vehicle with different sensors. This car can serve as a small scale simulator for ADAS control development and also can be used in academia.

Real-Time Implementation of Camera-Based Detection Algorithm for ADAS application

In this project, the main focus is on the application of camera image information to getting obstacle information such as the distance from the vehicle to the target vehicle needed to perform functionalities such as autonomous emergency braking (AEB) and mapping the detected lanes in an image to vehicle coordinates for lane Keep Assistance (LKA). Since the obstacle information based on vision system isn’t as accurate as other sensors like radar, Lidar or ultrasonic sensors, this project proposes a method to develop a test setup that can be used to validate the accuracy of the detection performed by a camera between the ego car and the target car along with detecting the lane. Such a setup makes it easy to test out ADAS enabling algorithms that uses the monocular camera-based information to help driver to avoid collision such as automatic braking or assist in driving by performing lane keep assist. The validation of the detection is done based on a virtual environment generated using PreScan as well as in real world by using a camera on a 1/10th scale vehicle. The project also dives into the aspects of building the scale model equipped with necessary sensors such that it facilitates ease of integration with simulation software and for testing ADAS and Autonomous Vehicle (AV) related control strategies.

List of Major Industry Related Projects

• Wheel-Motor Control Algorithm for Commercial Vehicle Applications (9/2018-8/2019, Hyundai-Kia Motor Company)

• Steering Control Parameter Optimization (5/2018-4/2020, Ford Motor Company)

• Simulation Techniques for Assessment of Advance Driver Assistance Systems in Commercial Vehicle Applications (9/2016-8/2017, Hyundai-Kia Motor Company)

• Development of Vehicle Simulation Tool for Assessment of Advanced Driver Assistance Systems (Year 2) (5/2016-1/2017)

• Motor Torque Allocation to Enhance Performance of EV/HEV Applications (3/2016-2/2017, UMD)

• Enhancement of Electric Power Assist Steering (EPAS) Systems (Ford Motor Company, 7/2015 - 6/2017)

• Development of Vehicle Simulation Tool for Assessment of Advanced Driver Assistance Systems (Hyundai-NGV, 7/2014 - 12/2016.)

• Development of Advanced Brake Control Methodology to Enhance Vehicle Stability for EV/HEV Applications (2011~2014, TRW Automotive)

• Investigation of Torque Vectoring System for Vehicle Handling Enhancement (2011~2012, Hyundai-Kia Motor Company)

• Investigation of Motion Planning Algorithms for High Speed Autonomous Vehicle Applications (2011, UMD)

• Development of Brake Blending Techniques for EV/HEV Applications (2010-2012, UMD)

• Development of a Vehicle-Trailer Straight-Line Back-Up Control Systems (2009~2011, Ford Motor company)

• Development of an Improved Lithium-ion Battery Model Incorporating Thermal Effects (2009, UMD)

• Investigation of Effectiveness of Trailbraking for Active Vehicle Safety (2007~2010, Ford Motor Company)

• Development of a 3-D Vehicle Animation Tool Using MATLAB (2006~2008, Ford Motor Company)

• Research Support for the AGMV Hybrid Control Refinement IRAD (General Dynamics Land Systems)

• Investigation of Suspension Characteristic and Development of Effective Suspension Model for Vehicle Roll Control (UMD HP-CEEP)

• Adaptation of Roll Center Migration on Parametric Vehicle Model from Multi-Body Vehicle Model (UMD)

• Design and Analysis of Brake System for the Low Mass Vehicle, (UMD IAVS)

• Integration of Active Suspension with Steering and Brake Systems for Vehicle Handling Enhancement, (Rackham Faculty Grant and Fellowships)

• Development of Active Steering/Wheel Torque Controller for the Rollover Limit Maneuver (OVPR)

• Investigation of Active Steering Control at Large Roll Angle (Ford Motor Company)

• Suspension Design and Dynamic Analysis of Lightweight Vehicle, (UMD IAVS)

• Dynamic Normal Force Control for Vehicle Handling (UMD HP-CEEP) Design of Tuned Vibration Absorbers for Agricultural Tractors (UMD)