A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Cooperative Perception for Social Driving in Connected Vehicle Traffic

The development of autonomous vehicle technology has moved to the center of automotive research in recent decades. In the foreseeable future, road vehicles at all levels of automation and connectivity will be required to operate safely in a hybrid traffic where human operated vehicles (HOVs) and fully and semi-autonomous vehicles (AVs) coexist. Having an accurate and reliable perception of the road is an important requirement for achieving this objective. This dissertation addresses some of the associated challenges via developing a human-like social driver model and devising a decentralized cooperative perception framework. A human-like driver model can aid the development of AVs by building an understanding of interactions among human drivers and AVs in a hybrid traffic, therefore facilitating an efficient and safe integration. The presented social driver model categorizes and defines the driver\u27s psychological decision factors in mathematical representations (target force, object force, and lane force). A model predictive control (MPC) is then employed for the motion planning by evaluating the prevailing social forces and considering the kinematics of the controlled vehicle as well as other operating constraints to ensure a safe maneuver in a way that mimics the predictive nature of the human driver\u27s decision making process. A hierarchical model predictive control structure is also proposed, where an additional upper level controller aggregates the social forces over a longer prediction horizon upon the availability of an extended perception of the upcoming traffic via vehicular networking. Based on the prediction of the upper level controller, a sequence of reference lanes is passed to a lower level controller to track while avoiding local obstacles. This hierarchical scheme helps reduce unnecessary lane changes resulting in smoother maneuvers. The dynamic vehicular communication environment requires a robust framework that must consistently evaluate and exploit the set of communicated information for the purpose of improving the perception of a participating vehicle beyond the limitations. This dissertation presents a decentralized cooperative perception framework that considers uncertainties in traffic measurements and allows scalability (for various settings of traffic density, participation rate, etc.). The framework utilizes a Bhattacharyya distance filter (BDF) for data association and a fast covariance intersection fusion scheme (FCI) for the data fusion processes. The conservatism of the covariance intersection fusion scheme is investigated in comparison to the traditional Kalman filter (KF), and two different fusion architectures: sensor-to-sensor and sensor-to-system track fusion are evaluated. The performance of the overall proposed framework is demonstrated via Monte Carlo simulations with a set of empirical communications models and traffic microsimulations where each connected vehicle asynchronously broadcasts its local perception consisting of estimates of the motion states of self and neighboring vehicles along with the corresponding uncertainty measures of the estimates. The evaluated framework includes a vehicle-to-vehicle (V2V) communication model that considers intermittent communications as well as a model that takes into account dynamic changes in an individual vehicle’s sensors’ FoV in accordance with the prevailing traffic conditions. The results show the presence of optimality in participation rate, where increasing participation rate beyond a certain level adversely affects the delay in packet delivery and the computational complexity in data association and fusion processes increase without a significant improvement in the achieved accuracy via the cooperative perception. In a highly dense traffic environment, the vehicular network can often be congested leading to limited bandwidth availability at high participation rates of the connected vehicles in the cooperative perception scheme. To alleviate the bandwidth utilization issues, an information-value discriminating networking scheme is proposed, where each sender broadcasts selectively chosen perception data based on the novelty-value of information. The potential benefits of these approaches include, but are not limited to, the reduction of bandwidth bottle-necking and the minimization of the computational cost of data association and fusion post processing of the shared perception data at receiving nodes. It is argued that the proposed information-value discriminating communication scheme can alleviate these adverse effects without sacrificing the fidelity of the perception

Over-the-Horizon Awareness for Advanced Driver Assistance Systems: the TrafficFilter and microSlotted 1-Persistence Flooding

Vehicle-to-vehicle communications (V2V) is a promising technique for Advanced Driver Assistance Systems to increase traffic safety and efficiency. A proposed system is the Congestion Assistant, which supports drivers when approaching and driving in traffic congestion. Studies have shown great potential for such systems to reduce the impact of congestion, even at low penetration. However, these studies assumed complete and instantaneous knowledge regarding position and velocity of vehicles ahead. This paper refines and analyses the TrafficFilter, designed to supply the required information to the Congestion Assistant. Vehicles collaboratively build a so-called TrafficMap, providing over-the-horizon awareness. To this end, an improvement to the Slotted 1-Persistence Flooding called microSlotted 1-Persistence Flooding is proposed and evaluated. In a simulation study the TrafficFilter is found to be a viable system to build over-the-horizon awareness for future Advanced Driver Assistance Systems like the Congestion Assistant, without triggering the phenomenon known as Broadcast Storm

Towards the sustainability of road transport through the introduction of AV technology

The paper investigates the potential for Level 2 autonomous vehicle (AV) technology to improve four prevailing sustainability issues specifically on highways: high congestion levels, increasing accident rates, high CO_2 emissions and poor journey time reliability. Co-operative Adaptive Cruise Control (CACC) shows potential to achieve high volume co-operative driving on highways by controlling these parameters and forming vehicle platoons. Accident rates, CO_2 emissions and journey times can be reduced as a result. The risks of platooning are discussed and a minimum safe platoon headway is established to mitigate the risk of vehicle platoon collisions. This headway is applied to a real highway case study demonstrating the potential to increase notional highway design capacity from 3,600 vehicles per hour (vph) to 9,213 vph, with significant sustainability improvements possible. Recommendations are made to complete a number of policy implementation and technology development tasks aimed to create the best chance of achieving the identified sustainability benefits within a 20 year timeframe.This is the author accepted manuscript. The final version is available from ICE Publishing via https://doi.org/10.1680/ensu.14.0005

Situational Awareness Enhancement for Connected and Automated Vehicle Systems

Recent developments in the area of Connected and Automated Vehicles (CAVs) have boosted the interest in Intelligent Transportation Systems (ITSs). While ITS is intended to resolve and mitigate serious traffic issues such as passenger and pedestrian fatalities, accidents, and traffic congestion; these goals are only achievable by vehicles that are fully aware of their situation and surroundings in real-time. Therefore, connected and automated vehicle systems heavily rely on communication technologies to create a real-time map of their surrounding environment and extend their range of situational awareness. In this dissertation, we propose novel approaches to enhance situational awareness, its applications, and effective sharing of information among vehicles.;The communication technology for CAVs is known as vehicle-to-everything (V2x) communication, in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) have been targeted for the first round of deployment based on dedicated short-range communication (DSRC) devices for vehicles and road-side transportation infrastructures. Wireless communication among these entities creates self-organizing networks, known as Vehicular Ad-hoc Networks (VANETs). Due to the mobile, rapidly changing, and intrinsically error-prone nature of VANETs, traditional network architectures are generally unsatisfactory to address VANETs fundamental performance requirements. Therefore, we first investigate imperfections of the vehicular communication channel and propose a new modeling scheme for large-scale and small-scale components of the communication channel in dense vehicular networks. Subsequently, we introduce an innovative method for a joint modeling of the situational awareness and networking components of CAVs in a single framework. Based on these two models, we propose a novel network-aware broadcast protocol for fast broadcasting of information over multiple hops to extend the range of situational awareness. Afterward, motivated by the most common and injury-prone pedestrian crash scenarios, we extend our work by proposing an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection for vulnerable road users. Finally, as humans are the most spontaneous and influential entity for transportation systems, we design a learning-based driver behavior model and integrate it into our situational awareness component. Consequently, higher accuracy of situational awareness and overall system performance are achieved by exchange of more useful information

Intelligent Transportation Systems, Hybrid Electric Vehicles, Powertrain Control, Cooperative Adaptive Cruise Control, Model Predictive Control

Information obtainable from Intelligent Transportation Systems (ITS) provides the possibility of improving the safety and efficiency of vehicles at different levels. In particular, such information has the potential to be utilized for prediction of driving conditions and traffic flow, which allows us to improve the performance of the control systems in different vehicular applications, such as Hybrid Electric Vehicles (HEVs) powertrain control and Cooperative Adaptive Cruise Control (CACC). In the first part of this work, we study the design of an MPC controller for a Cooperative Adaptive Cruise Control (CACC) system, which is an automated application that provides the drivers with extra benefits, such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as interfering vehicles cutting-into the CACC platoons or hard braking by leading cars. To address this problem, we first propose a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme. Then, the predicted trajectory of each vehicle in the adjacent lanes is used to estimate the probability of that vehicle cutting-into the CACC platoon. To consider the calculated probability in control system decisions, a Stochastic Model Predictive Controller (SMPC) needs to be designed which incorporates this cut-in probability, and enhances the reaction against the detected dangerous cut-in maneuver. However, in this work, we propose an alternative way of solving this problem. We convert the SMPC problem into modeling the CACC as a Stochastic Hybrid System (SHS) while we still use a deterministic MPC controller running in the only state of the SHS model. Finally, we find the conditions under which the designed deterministic controller is stable and feasible for the proposed SHS model of the CACC platoon. In the second part of this work, we propose to improve the performance of one of the most promising realtime powertrain control strategies, called Adaptive Equivalent Consumption Minimization Strategy (AECMS), using predicted driving conditions. In this part, two different real-time powertrain control strategies are proposed for HEVs. The first proposed method, including three different variations, introduces an adjustment factor for the cost of using electrical energy (equivalent factor) in AECMS. The factor is proportional to the predicted energy requirements of the vehicle, regenerative braking energy, and the cost of battery charging and discharging in a finite time window. Simulation results using detailed vehicle powertrain models illustrate that the proposed control strategies improve the performance of AECMS in terms of fuel economy by 4\%. Finally, we integrate the recent development in reinforcement learning to design a novel multi-level power distribution control. The proposed controller reacts in two levels, namely high-level and low-level control. The high-level control decision estimates the most probable driving profile matched to the current (and near future) state of the vehicle. Then, the corresponding low-level controller of the selected profile is utilized to distribute the requested power between Electric Motor (EM) and Internal Combustion Engine (ICE). This is important because there is no other prior work addressing this problem using a controller which can adjust its decision to the driving pattern. We proposed to use two reinforcement learning agents in two levels of abstraction. The first agent, selects the most optimal low-level controller (second agent) based on the overall pattern of the drive cycle in the near past and future, i.e., urban, highway and harsh. Then, the selected agent by the high-level controller (first agent) decides how to distribute the demanded power between the EM and ICE. We found that by carefully designing a training scheme, it is possible to effectively improve the performance of this data-driven controller. Simulation results show up to 6\% improvement in fuel economy compared to the AECMS

Connected and Automated Vehicles in Urban Transportation Cyber-Physical Systems

Understanding the components of Transportation Cyber-Physical Systems (TCPS), and inter-relation and interactions among these components are key factors to leverage the full potentials of Connected and Automated Vehicles (CAVs). In a connected environment, CAVs can communicate with other components of TCPS, which include other CAVs, other connected road users, and digital infrastructure. Deploying supporting infrastructure for TCPS, and developing and testing CAV-specific applications in a TCPS environment are mandatory to achieve the CAV potentials. This dissertation specifically focuses on the study of current TCPS infrastructure (Part 1), and the development and verification of CAV applications for an urban TCPS environment (Part 2). Among the TCPS components, digital infrastructure bears sheer importance as without connected infrastructure, the Vehicle-to-Infrastructure (V2I) applications cannot be implemented. While focusing on the V2I applications in Part 1, this dissertation evaluates the current digital roadway infrastructure status. The dissertation presents a set of recommendations, based on a review of current practices and future needs. In Part 2, To synergize the digital infrastructure deployment with CAV deployments, two V2I applications are developed for CAVs for an urban TCPS environment. At first, a real-time adaptive traffic signal control algorithm is developed, which utilizes CAV data to compute the signal timing parameters for an urban arterial in the near-congested traffic condition. The analysis reveals that the CAV-based adaptive signal control provides operational benefits to both CVs and non-CVs with limited data from 5% CVs, with 5.6% average speed increase, and 66.7% and 32.4% average maximum queue length and stopped delay reduction, respectively, on a corridor compared to the actuated coordinated scenario. The second application includes the development of a situation-aware left-turning CAV controller module, which optimizes CAV speed based on the follower driver\u27s aggressiveness. Existing autonomous vehicle controllers do not consider the surrounding driver\u27s behavior, which may lead to road rage, and rear-end crashes. The analysis shows that the average travel time reduction for the scenarios with 600, 800 and 1000 veh/hr/lane opposite traffic stream are 61%, 23%, and 41%, respectively, for the follower vehicles, if the follower driver\u27s behavior is considered by CAVs

Robust distributed resource allocation for cellular vehicle-to-vehicle communication

Mit Release 14 des LTE Standards unterstützt dieser die direkte Fahrzeug-zu-Fahrzeug-Kommunikation über den Sidelink. Diese Dissertation beschäftigt sich mit dem Scheduling Modus 4, einem verteilten MAC-Protokoll ohne Involvierung der Basisstation, das auf periodischer Wiederverwendung von Funkressourcen aufbaut. Der Stand der Technik und eine eigene Analyse des Protokolls decken verschiedene Probleme auf. So wiederholen sich Kollisionen von Paketen, wodurch manche Fahrzeuge für längere Zeit keine sicherheitskritischen Informationen verbreiten können. Kollisionen entstehen vermehrt auch dadurch, dass Hidden-Terminal-Probleme in Kauf genommen werden oder veränderliche Paketgrößen und -raten schlecht unterstützt werden. Deshalb wird ein Ansatz namens "Scheduling based on Acknowledgement Feedback Exchange" vorgeschlagen. Zunächst wird eine Funkreservierung in mehrere ineinander verschachtelte Unter-Reservierungen mit verschiedenen Funkressourcen unterteilt, was die Robustheit gegenüber wiederholenden Kollisionen erhöht. Dies ist die Grundlage für eine verteilte Staukontrolle, die die Periodizitätseigenschaft nicht verletzt. Außerdem können so veränderliche Paketgrößen oder -raten besser abgebildet werden. Durch die periodische Wiederverwendung können Acknowledgements für Funkressourcen statt für Pakete ausgesendet werden. Diese können in einer Bitmap in den Padding-Bits übertragen werden. Mittels der Einbeziehung dieser Informationen bei der Auswahl von Funkressourcen können Hidden-Terminal-Probleme effizient vermieden werden, da die Acknowledgements auch eine Verwendung dieser Funkressource ankündigen. Kollisionen können nun entdeckt und eine Wiederholung vermieden werden. Die Evaluierung des neuen MAC-Protokolls wurde zum großen Teil mittels diskreter-Event-Simulationen durchgeführt, wobei die Bewegung jedes einzelnen Fahrzeuges simuliert wurde. Der vorgeschlagene Ansatz führt zu einer deutlich erhöhten Paketzustellrate. Die Verwendung einer anwendungsbezogenen Awareness-Metrik zeigt, dass die Zuverlässigkeit der Kommunikation durch den Ansatz deutlich verbessert werden kann. Somit zeigt sich der präsentierte Ansatz als vielversprechende Lösung für die erheblichen Probleme, die der LTE Modus 4 mit sich bringt.The LTE Standard added support for a direct vehicle-to-vehicle communication via the Sidelink with Release 14. This dissertation focuses on the scheduling Mode 4, a distributed MAC protocol without involvement of the base station, which requires the periodic reuse of radio resources. The state of the art and a own analysis of this protocol unveil multiple problems. For example, packet collisions repeat in time, so that some vehicles are unable to distribute safety-critical information for extended periods of time. Collisions also arise due to the hidden-terminal problem, which is simply put up with in Mode 4. Additionally, varying packet sizes or rates can hardly be supported. Consequently, an approach called "Scheduling based on Acknowledgement Feedback Exchange" is proposed. Firstly, a reservation of radio resources is split into multiple, interleaved sub-reservations that use different radio resources. This increases the robustness against repeating collisions. It is also the basis for a distributed congestion control that does not violate the periodicity. Moreover, different packet rates or sizes can be supported. The periodic reuse of radio resources enables the transmission of acknowledgements for radio resources instead of packets. These can be transmitted in a bitmap inside the padding bits. Hidden-terminal problems can be mitigated by considering the acknowledgements when selecting radio resources as they announce the use of these radio resources. Collisions can also be detected and prevented from re-occurring. The evaluation of the MAC protocol is mostly performed using discrete-event simulations, which model the movement of every single vehicle. The presented approach leads to a clear improvement of the packet delivery rate. The use of an application-oriented metric shows that the communication robustness can be improved distinctly. The proposed approach hence presents itself as a promising solution for the considerable problems of LTE Mode 4