https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Review of Motion Planning Techniques for Automated Vehicles

Under the Connected Vehicles (CV) environment, it is possible to create a Cooperative Vehicle Intersection Control (CVIC) system that enables cooperation between vehicles and infrastructure for effective intersection operations and management when all vehicles are fully automated. Assuming such a CVIC environment, this paper proposed a CVIC algorithm that does not require a traffic signal. The CVIC algorithm was designed to manipulate individual vehicles' maneuvers so that vehicles can safely cross the intersection without colliding with other vehicles. By eliminating the potential overlaps of vehicular trajectories coming from all conflicting approaches at the intersection, the CVIC algorithm seeks a safe maneuver for every vehicle approaching the intersection and manipulates each of them. An additional algorithm was designed to deal with the system failure cases resulting from inevitable trajectory overlaps at the intersection and infeasible solutions. A simulation-based case study implemented on a hypothetical four-way single-lane approach intersection under varying congestion conditions showed that the CVIC algorithm significantly improved intersection performance compared with conventional actuated intersection control: 99% and 33% of stop delay and total travel time reductions, respectively, were achieved. In addition, the CVIC algorithm significantly improved air quality and energy savings: 44% reductions of CO2 and 44% savings of fuel consumption.

Development and Evaluation of a Cooperative Vehicle Intersection Control Algorithm Under the Connected Vehicles Environment

Connected and automated vehicles (CAVs) have the potential to improve safety by reducing and mitigating traffic accidents. They can also provide opportunities to reduce transportation energy consumption and emissions by improving traffic flow. Vehicle communication with traffic structures and traffic lights can allow individual vehicles to optimize their operation and account for unpredictable changes. This paper summarizes the developments and the research trends in coordination with the CAVs that have been reported in the literature to date. Remaining challenges and potential future research directions are also discussed.

A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps

Transportation research. part c, emerging technologies

https://nottingham-repository.worktribe.com/preview/2315217/Multi_Time_Series_Anomaly_Detection_for_Industry_4_0__Neurocomputing_.pdf

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.

/pdf/an-intelligent-v2i-based-traffic-management-system-33geyycs9s.pdf

An Intelligent V2I-Based Traffic Management System

Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.

A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

A major research topic in intelligent transportation systems (ITSs) is the development of systems that will be capable of controlling the flow of vehicular traffic through crossroads, particularly in urban environments. This could significantly reduce traffic jams, since autonomous vehicles would be capable of calculating the optimal speed to maximize the number of cars driving through the intersection. We describe the use of vehicle-to-vehicle (V2V) communications to determine the position and speed of the vehicles in an environment around a crossroad. These data are used to estimate the intersection point, and a fuzzy controller then modifies the speed of the cars without right of way according to the speed of the car with right of way. Experimental tests conducted with two mass-produced cars on a real circuit at the facilities of the Instituto de Automa?tica Industrial, Consejo Superior de Investigaciones Cienti?ficas, Madrid, Spain, gave excellent results.

/pdf/controller-for-urban-intersections-based-on-wireless-12ta8movf5.pdf

Controller for Urban Intersections Based on Wireless Communications and Fuzzy Logic

The book provides a systematicoverview of Intelligent Transportation Systems (ITS). First, it includes aninsight into thereference architectures developed within the main EU research projects. Then, it delves into each of the layers of such architectures, from physical to application layer, describing the technological issues which are being currently faced by some of the most important ITS research groups. The bookconcludes with some end user services and applications deployed by industrial partners. This book is a well-balanced combination of academic contributions and industrial applications in the field of Intelligent Transportation Systems. The most representative technologies and research results achieved by some of the most relevant research groups working on ITS,collated to show the chances of generating industrial solutions to be deployed in real transportation environments.

Enrique Onieva

Papers

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study

An Intelligent V2I-Based Traffic Management System

A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

Controller for Urban Intersections Based on Wireless Communications and Fuzzy Logic

Intelligent Transport Systems: Technologies and Applications