http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

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

Safety critical systems involve the tight coupling between potentially conflicting control objectives and safety constraints. As a means of creating a formal framework for controlling systems of this form, and with a view toward automotive applications, this paper develops a methodology that allows safety conditions—expressed as control barrier functions —to be unified with performance objectives—expressed as control Lyapunov functions—in the context of real-time optimization-based controllers. Safety conditions are specified in terms of forward invariance of a set, and are verified via two novel generalizations of barrier functions; in each case, the existence of a barrier function satisfying Lyapunov-like conditions implies forward invariance of the set, and the relationship between these two classes of barrier functions is characterized. In addition, each of these formulations yields a notion of control barrier function (CBF), providing inequality constraints in the control input that, when satisfied, again imply forward invariance of the set. Through these constructions, CBFs can naturally be unified with control Lyapunov functions (CLFs) in the context of a quadratic program (QP); this allows for the achievement of control objectives (represented by CLFs) subject to conditions on the admissible states of the system (represented by CBFs). The mediation of safety and performance through a QP is demonstrated on adaptive cruise control and lane keeping, two automotive control problems that present both safety and performance considerations coupled with actuator bounds.

https://ieeexplore.ieee.org/ielaam/9/7993115/7782377-aam.pdf

Control Barrier Function Based Quadratic Programs for Safety Critical Systems

The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.

Influence of connected and autonomous vehicles on traffic flow stability and throughput

Intelligent vehicle cooperation based on reliable communication systems contributes not only to reducing traffic accidents but also to improving traffic flow. Adaptive cruise control (ACC) systems can gain enhanced performance by adding vehicle-vehicle wireless communication to provide additional information to augment range sensor data, leading to cooperative ACC (CACC). This paper presents the design, development, implementation, and testing of a CACC system. It consists of two controllers, one to manage the approaching maneuver to the leading vehicle and the other to regulate car-following once the vehicle joins the platoon. The system has been implemented on four production Infiniti M56s vehicles, and this paper details the results of experiments to validate the performance of the controller and its improvements with respect to the commercially available ACC system.

/pdf/cooperative-adaptive-cruise-control-in-real-traffic-35mggeon75.pdf

Cooperative Adaptive Cruise Control in Real Traffic Situations

The complexity of these intelligent vehicle control systems is however in contradiction to the increasing demand for reliability and safety. To improve fault management often redundant components and fault-tolerant controllers are implemented in ADASs. In practice, it is however difficult to choose the right measures and to validate their effectiveness. Currently, simulations and prototype test drives are used to validate an ADAS, but these tests can be unreliable and costly. An efficient methodology and new design tools are thus required for validation of the safety and reliability of an ADAS under the influence of a variety of faults.

/pdf/validation-of-advanced-driver-assistance-systems-with-vehil-48y3sg03ed.pdf

Validation of Advanced Driver Assistance Systems with VEHIL

Collision avoidance system and method and respective computer program product

Cooperative automated driving (CAD) combines autonomous driving with cooperative driving, thereby yielding a powerful approach to improve traffic efficiency and safety. A very well-known example of CAD is platooning. However, when extending this one-dimensional application to two-dimensional maneuvering, covering a large number of traffic scenarios while also including safety threats imposed by other traffic or failing components of the automation system, a complex control system architecture may arise. To address this challenge, an agent-based control system architecture is proposed employing explicit decision making. This architecture is scalable with respect to the number of traffic scenarios that can be handled, capable of including safety features, and provides the flexibility to adopt various controller design approaches at the same time.

Cooperative Automated Driving: From Platooning to Maneuvering.

In this paper, non-linear estimators and feed-forward actions are designed to solve a vehicular platoon control problem. In particular, these estimators are useful in implementation of a Multi-Layer Consensus Seeking approach to the platooning problem. In the Multi-Layer approach the feed-forward part is built separately from the feedback part, based on desired trajectories. This opens the way to study different methods to compute this information. In this work, sliding mode algorithms are employed to generate the desired trajectories and also to compute the feed-forward control input of each vehicle. In this way, a velocity-dependent inter-vehicle spacing policy can be realized without using derivatives of the vehicular acceleration to compute the feed-forward input. Furthermore, a full-state feedback implementation scheme is discussed. The vehicles are modeled with heterogeneous linear longitudinal dynamics. The proposed algorithms are validated through experiments on automated cars.

Nonlinear multi-layer consensus seeking of vehicular platoons

In recent years, highway capacity has become a limiting factor, regularly causing traffic jams. Obviously, the road capacity can be increased by decreasing the inter-vehicle following distance di . As a consequence, however, vehicle automation in longitudinal direction is required in order to still guarantee safety. To this end, Adaptive Cruise Control could be used, which is based on measurement of the inter-vehicle distance and the relative velocity by means of radar. It has however been shown [1] that this will amplify disturbances in upstream direction at short time gaps, causing so-called ghost traffic jams. Application of data exchange by means of wireless communication in addition, is shown to be able to attenuate these disturbances. This is called Cooperative Adaptive Cruise Control (CACC), illustrated in Figure 1 for a one-vehicle look-ahead communication structure.

https://pure.tue.nl/ws/files/3429325/5647132661327458.pdf

Jeroen Ploeg

Papers

Validation of Advanced Driver Assistance Systems with VEHIL

Collision avoidance system and method and respective computer program product

Cooperative Automated Driving: From Platooning to Maneuvering.

Nonlinear multi-layer consensus seeking of vehicular platoons

String stability of vehicle platoons