Indianapolis / E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.

E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.<br>

For several years, there has been a remarkable increase in efforts to develop an autonomous car. Autonomous car systems combine various techniques of recognizing the environment with the help of the sensors and could drastically bring down the number of accidents on road by removing human conduct errors related to driver inattention and poor driving choices. In this research thesis, an algorithm for jointly ego-vehicle motion and road geometry estimation for Advanced Driver Assistance Systems (ADAS) is developed. The measurements are obtained from the inertial sensors, wheel speed sensors, steering wheel angle sensors, and camera. An Unscented Kalman Filter (UKF) is used for estimating the states of the non-linear system because UKF estimates the state in a simplified way without using complex computations. The proposed algorithm has been tested on a winding and straight road. The robustness and functioning of our algorithm have been demonstrated by conducting experiments involving the addition of noise to the measurements, reducing the process noise covariance matrix, and increasing the measurement noise covariance matrix and through these tests, we gained more trust in the working of our tracker. For evaluation, each estimated parameter has been compared with the reference signal which shows that the estimated signal matches the reference signal very well in both scenarios. We also compared our joint algorithm with individual ego-vehicle and road geometry algorithms. The results clearly show that better estimates are obtained from our algorithm when estimated jointly instead of estimating separately.