Integrated Longitudinal and Lateral Networked Control System Design for Vehicle Platooning.

This paper investigates platoon control of vehicles via the wireless communication network. An integrated longitudinal and lateral control approaches for vehicle platooning within a designated lane is proposed. Firstly, the longitudinal control aims to regulate the speed of the follower vehicle on the leading vehicle while maintaining the inter-distance to the desired value which may be chosen proportional to the vehicle speed. Thus, based on Lyapunov candidate function, sufficient stability conditions formulated in BMIs terms are proposed. For the general objective of string stability and robust platoon control to be achieved simultaneously, the obtained controller is complemented by additional conditions established for guaranteeing string stability. Furthermore, constraints such as actuator saturation, and controller constrained information are also considered in control design. Secondly, a multi-model fuzzy controller is developed to handle the vehicle lateral control. Its objective is to maintain the vehicle within the road through steering. The design conditions are strictly expressed in terms of LMIs which can be efficiently solved with available numerical solvers. The effectiveness of the proposed control method is validated under the CarSim software package.

Proprioceptive Sensors' Fault Tolerant Control Strategy for an Autonomous Vehicle.

In this contribution, a fault-tolerant control strategy for the longitudinal dynamics of an autonomous vehicle is presented. The aim is to be able to detect potential failures of the vehicle's speed sensor and then to keep the vehicle in a safe state. For this purpose, the separation principle, composed of a static output feedback controller and fault estimation observers, is designed. Indeed, two observer techniques were proposed: the proportional and integral observer and the descriptor observer. The effectiveness of the proposed scheme is validated by means of the experimental demonstrator of the VEDECOM (Véhicle Décarboné et Communinicant) Institut.

Fault Detection and Isolation via the Interacting Multiple Model Approach Applied to Drive-By-Wire Vehicles.

The place of driving assistance systems is currently increasing drastically for road vehicles. Paving the road to the fully autonomous vehicle, the drive-by-wire technology could improve the potential of the vehicle control. The implementation of these new embedded systems is still limited, mainly for reliability reasons, thus requiring the development of diagnostic mechanisms. In this paper, we investigate the detection and the identification of sensor and actuator faults for a drive-by-wire road vehicle. An Interacting Multiple Model approach is proposed, based on a non-linear vehicle dynamics observer. The adequacy of different probabilistic observers is discussed. The results, based on experimental vehicle signals, show a fast and robust identification of sensor faults while the actuator faults are more challenging.

A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries.

Advancements in data collection and processing methods have produced large databases containing high quality vehicular data. Despite this, conventional vehicle-vehicle collisions remain difficult to identify due to their rarity. Therefore, there is a need to identify potential collisions given the introduction of these new data collection methods. Surrogate indicators are a popular methods utilised to identify such events, however, the type of surrogate that can be used depends heavily on the type of data collection method. Though most surrogate indicators are used at different road geometries, there is evidence to suggest that some surrogate indicators may perform better than others at a given geometry. This review provides two key contributions to the body of literature. Firstly, a review of kinematic surrogates is put forward, along with a discussion on the whether these surrogates can be contextualised at different road geometries. Secondly, an extensive analysis and discussion of observer-based and video processed surrogate indicators, the collision types they aim to identify and the geometries they have been used at previously were analysed and advantages and disadvantages of the surrogates have been presented for future use. To do this, intersections, highways and roundabouts were selected and divided into geometry subtypes (i.e. three-legged and four-legged intersection) and segments (i.e. approaches to intersections and internal to the intersection) based on the likelihood of crash types and pre-crash manoeuvres occurring in that segment. Due to the lack of research around the use of kinematic triggers at road geometries, it is difficult to advocate for the use of any given trigger over another at a given geometry. Furthermore, it was found that kinematic triggers cannot accurately identify conflicts from naturalistic driving data and require the use of advanced statistical techniques such as machine learning to increase accuracy. A brief analysis of threshold identification techniques was also performed. Several future works have been put forward including the introduction of surrogates which capture conflict severity and the role of surrogate indicators in connected and automated vehicle environments.

Correction: A framework for testing independence between lane change and cooperative intelligent transportation system.

[This corrects the article DOI: 10.1371/journal.pone.0229289.].

Autonomous Vehicles and Vulnerable Road-Users-Important Considerations and Requirements Based on Crash Data from Two Countries.

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

Hybrid pointer networks for traveling salesman problems optimization.

In this work, we proposed a hybrid pointer network (HPN), an end-to-end deep reinforcement learning architecture is provided to tackle the travelling salesman problem (TSP). HPN builds upon graph pointer networks, an extension of pointer networks with an additional graph embedding layer. HPN combines the graph embedding layer with the transformer's encoder to produce multiple embeddings for the feature context. We conducted extensive experimental work to compare HPN and Graph pointer network (GPN). For the sack of fairness, we used the same setting as proposed in GPN paper. The experimental results show that our network significantly outperforms the original graph pointer network for small and large-scale problems. For example, it reduced the cost for travelling salesman problems with 50 cities/nodes (TSP50) from 5.959 to 5.706 without utilizing 2opt. Moreover, we solved benchmark instances of variable sizes using HPN and GPN. The cost of the solutions and the testing times are compared using Linear mixed effect models. We found that our model yields statistically significant better solutions in terms of the total trip cost. We make our data, models, and code publicly available https://github.com/AhmedStohy/Hybrid-Pointer-Networks.

A framework for testing independence between lane change and cooperative intelligent transportation system.

Cooperative Intelligent Transportation Systems (C-ITS) are being deployed in several cities around the world. We are preparing for the largest Field Operational Test (FOT) in Australia to evaluate C-ITS safety benefits. Two of the safety benefit hypotheses we formulated assume a dependency between lane changes and C-ITS warnings displayed on the Human Machine Interface (HMI) during safety events. Lane change detection is done by processing many predictors from several sensors at the time of the safety event. However, in our planned FOT, the participating vehicles are only equipped with the vehicle C-ITS and the IMU. Therefore, in this paper, we propose a framework to test lane change and C-ITS dependency. In this framework, we train a random forest classifier using data collected from the IMU to detect lane changes. Consequently, the random forest output probabilities of the testing data in case of C-ITS and control are used to construct a 2x2 contingency table. Then we develop a permutation test to calculate the null hypothesis needed to test the independence of the lane change during safety events and the C-ITS.