Driver Distraction Identification with an Ensemble of Convolutional Neural Networks

The World Health Organization (WHO) reported 1.25 million deaths yearly due to road traffic accidents worldwide and the number has been continuously increasing over the last few years. Nearly fifth of these accidents are caused by distracted drivers. Existing work of distracted driver detection is concerned with a small set of distractions (mostly, cell phone usage). Unreliable ad-hoc methods are often used.In this paper, we present the first publicly available dataset for driver distraction identification with more distraction postures than existing alternatives. In addition, we propose a reliable deep learning-based solution that achieves a 90% accuracy. The system consists of a genetically-weighted ensemble of convolutional neural networks, we show that a weighted ensemble of classifiers using a genetic algorithm yields in a better classification confidence. We also study the effect of different visual elements in distraction detection by means of face and hand localizations, and skin segmentation. Finally, we present a thinned version of our ensemble that could achieve 84.64% classification accuracy and operate in a real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949

A Voting Algorithm for Dynamic Object Identification and Pose Estimation

While object identification enables autonomous vehicles to detect and recognize objects from real-time images, pose estimation further enhances their capability of navigating in a dynamically changing environment. This thesis proposes an approach which makes use of keypoint features from 3D object models for recognition and pose estimation of dynamic objects in the context of self-driving vehicles. A voting technique is developed to vote out a suitable model from the repository of 3D models that offers the best match with the dynamic objects in the input image. The matching is done based on the identified keypoints on the image and the keypoints corresponding to each template model stored in the repository. A confidence score value is then assigned to measure the confidence with which the system can confirm the presence of the matched object in the input image. Being dynamic objects with complex structure, human models in the COCO-DensePose dataset, along with the DensePose deep-learning model developed by the Facebook research team, have been adopted and integrated into the system for 3D pose estimation of pedestrians on the road. Additionally, object tracking is performed to find the speed and location details for each of the recognized dynamic objects from consecutive image frames of the input video. This research demonstrates with experimental results that the use of 3D object models enhances the confidence of recognition and pose estimation of dynamic objects in the real-time input image. The 3D pose information of the recognized dynamic objects along with their corresponding speed and location information would help the autonomous navigation system of the self-driving cars to take appropriate navigation decisions, thus ensuring smooth and safe driving

Cooperative control of autonomous connected vehicles from a Networked Control perspective: Theory and experimental validation

Formation control of autonomous connected vehicles is one of the typical problems addressed in the general context of networked control systems. By leveraging this paradigm, a platoon composed by multiple connected and automated vehicles is represented as one-dimensional network of dynamical agents, in which each agent only uses its neighboring information to locally control its motion, while it aims to achieve certain global coordination with all other agents. Within this theoretical framework, control algorithms are traditionally designed based on an implicit assumption of unlimited bandwidth and perfect communication environments. However, in practice, wireless communication networks, enabling the cooperative driving applications, introduce unavoidable communication impairments such as transmission delay and packet losses that strongly affect the performances of cooperative driving. Moreover, in addition to this problem, wireless communication networks can suffer different security threats. The challenge in the control field is hence to design cooperative control algorithms that are robust to communication impairments and resilient to cyber attacks. The work aim is to tackle and solve these challenges by proposing different properly designed control strategies. They are validated both in analytical, numerical and experimental ways. Obtained results confirm the effectiveness of the strategies in coping with communication impairments and security vulnerabilities

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 2

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

Ensemble Learning for Fusion of Multiview Vision with Occlusion and Missing Information: Framework and Evaluations with Real-World Data and Applications in Driver Hand Activity Recognition

Multi-sensor frameworks provide opportunities for ensemble learning and sensor fusion to make use of redundancy and supplemental information, helpful in real-world safety applications such as continuous driver state monitoring which necessitate predictions even in cases where information may be intermittently missing. We define this problem of intermittent instances of missing information (by occlusion, noise, or sensor failure) and design a learning framework around these data gaps, proposing and analyzing an imputation scheme to handle missing information. We apply these ideas to tasks in camera-based hand activity classification for robust safety during autonomous driving. We show that a late-fusion approach between parallel convolutional neural networks can outperform even the best-placed single camera model in estimating the hands' held objects and positions when validated on within-group subjects, and that our multi-camera framework performs best on average in cross-group validation, and that the fusion approach outperforms ensemble weighted majority and model combination schemes