A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments

Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles

The recent and continuous improvement in the transportation field provides several different opportunities for enhancing safety and comfort in passenger vehicles. In this context, Adaptive Cruise Control (ACC) might provide additional benefits, including smoothness of the traffic flow and collision avoidance. In addition, Vehicle-to-Vehicle (V2V) communication may be exploited in the car-following model to obtain further improvements in safety and comfort by guaranteeing fast response to critical events. In this paper, firstly an Adaptive Model Predictive Control was developed for managing the Cooperative ACC scenario of two vehicles; as a second step, the safety analysis during a cut-in maneuver was performed, extending the platooning vehicles’ number to four. The effectiveness of the proposed methodology was assessed for in different driving scenarios such as diverse cruising speeds, steep accelerations, and aggressive decelerations. Moreover, the controller was validated by considering various speed profiles of the leader vehicle, including a real drive cycle obtained using a random drive cycle generator software. Results demonstrated that the proposed control strategy was capable of ensuring safety in virtually all test cases and quickly responding to unexpected cut-in maneuvers. Indeed, different scenarios have been tested, including acceleration and deceleration phases at high speeds where the control strategy successfully avoided any collision and stabilized the vehicle platoon approximately 20–30 s after the sudden cut-in. Concerning the comfort, it was demonstrated that improvements were possible in the aggressive drive cycle whereas different scenarios were found in the random cycle, depending on where the cut-in maneuver occurred

Cooperative Adaptive Cruise Control: A Gated Recurrent Unit Approach

Embedded artificial intelligence solutions are promising controllers for future sustainable and automated road vehicles. This study presents a deep learning-based approach combined with vehicle communication technology for the design of a real-time cooperative adaptive cruise control (CACC). A particular type of recurrent neural network has been selected, namely a gated recurrent unit (GRU). GRU exhibits improved learning performance in control problems such as the CACC since it avoids the vanishing gradient problems that characterize long time series. A GRU has been trained using ad-hoc CACC datasets build-up according to an optimal control policy, i.e. dynamic programming (DP), for a battery electric vehicle. In particular, DP optimizes the longitudinal speed trajectory of the Ego (Following) vehicle in CACC so to achieve energy savings and passenger comfort improvement. Results demonstrate that the Ego vehicle controlled by the trained GRU can achieve an eco-friendly driving in CACC without compromising passenger comfort and safety requirements. Unlike DP, GRU holds strong real-time potential. The performance of the proposed GRU approach for CACC is verified by benchmarking with the optimal performance obtained off-line using DP in several driving missions

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

Platoon Merging Approach Based on Hybrid Trajectory Planning and CACC Strategies

Currently, the increase of transport demands along with the limited capacity of the road network have increased traffic congestion in urban and highway scenarios. Technologies such as Cooperative Adaptive Cruise Control (CACC) emerge as efficient solutions. However, a higher level of cooperation among multiple vehicle platoons is needed to improve, effectively, the traffic flow. In this paper, a global solution to merge two platoons is presented. This approach combines: (i) a longitudinal controller based on a feed-back/feed-forward architecture focusing on providing CACC capacities and (ii) hybrid trajectory planning to merge platooning on straight paths. Experiments were performed using Tecnalia’s previous basis. These are the AUDRIC modular architecture for automated driving and the highly reliable simulation environment DYNACAR. A simulation test case was conducted using five vehicles, two of them executing the merging and three opening the gap to the upcoming vehicles. The results showed the good performance of both domains, longitudinal and lateral, merging multiple vehicles while ensuring safety and comfort and without propagating speed changes.This research was supported by the European Project SHOW from the Horizon 2020 program under Grant Agreement No. 875530

Exploring Smart Infrastructure Concepts to Improve the Reliability and Functionality of Safety Oriented Connected Vehicle Applications

Cooperative adaptive cruise control (CACC), a form of vehicle platooning, is a well known connected vehicle application. It extends adaptive cruise control (ACC) by incorporating vehicle-to-vehicle communications. A vehicle periodically broadcasts a small message that includes in the least a unique vehicle identifier, its current geo-location, speed, and acceleration. A vehicle might pay attention to the message stream of only the car ahead. While CACC is under intense study by the academic community, the vast majority of the relevant published literature has been limited to theoretical studies that make many simplifying assumptions. The research presented in this dissertation has been motivated by our observation that there is limited understanding of how platoons actually work under a range of realistic operating conditions. Our research includes a performance study of V2V communications based on actual V2V radios supplemented by simulation. These results are in turn applied to the analysis of CACC. In order to understand a platoon at scale, we resort to simulations and analysis using the ns3 simulator. Assessment criteria includes network reliability measures as well as application oriented measures. Network assessment involves latency and first and second order loss dynamics. CACC performance is based on stability, frequency of crashes, and the rate of traffic flow. The primary goal of CACC is to maximize traffic flow subject to a maximum allowed speed. This requires maintaining smaller inter-vehicle distances which can be problematic as a platoon can become unstable as the target headway between cars is reduced. The main contribution of this dissertation is the development and evaluation of two heuristic approaches for dynamically adapting headway both of which attempt to minimize the headway while ensure stability. We present the design and analysis of a centralized and a distributed implementation of the algorithm. Our results suggest that dynamically adapting the headway time can improve the overall platoon traffic flow without the platoon becoming unstable

Multi-Timescale Control and Communications with Deep Reinforcement Learning -- Part I: Communication-Aware Vehicle Control

An intelligent decision-making system enabled by Vehicle-to-Everything (V2X) communications is essential to achieve safe and efficient autonomous driving (AD), where two types of decisions have to be made at different timescales, i.e., vehicle control and radio resource allocation (RRA) decisions. The interplay between RRA and vehicle control necessitates their collaborative design. In this two-part paper (Part I and Part II), taking platoon control (PC) as an example use case, we propose a joint optimization framework of multi-timescale control and communications (MTCC) based on Deep Reinforcement Learning (DRL). In this paper (Part I), we first decompose the problem into a communication-aware DRL-based PC sub-problem and a control-aware DRL-based RRA sub-problem. Then, we focus on the PC sub-problem assuming an RRA policy is given, and propose the MTCC-PC algorithm to learn an efficient PC policy. To improve the PC performance under random observation delay, the PC state space is augmented with the observation delay and PC action history. Moreover, the reward function with respect to the augmented state is defined to construct an augmented state Markov Decision Process (MDP). It is proved that the optimal policy for the augmented state MDP is optimal for the original PC problem with observation delay. Different from most existing works on communication-aware control, the MTCC-PC algorithm is trained in a delayed environment generated by the fine-grained embedded simulation of C-V2X communications rather than by a simple stochastic delay model. Finally, experiments are performed to compare the performance of MTCC-PC with those of the baseline DRL algorithms

Mixing V2V- and non-V2V-equipped vehicles in car following

International audienceCooperative Adaptive Cruise Control (CACC) provides significant traffic flow improvements when a vehicle-to-vehicle (V2V) communication link exists with the preceding vehicle , but it degrades to an Adaptive Cruise Control (ACC) when this communication link is no longer available. This degradation occurs even if the information from another V2V-equipped vehicle ahead (different to the preceding one) is still available. This paper presents a novel car-following control system-Advanced Cooperative Adaptive Cruise Control (ACACC)-that benefits from the existing communication with this vehicle ahead in the string, reducing inter-vehicle gap whereas keeping string stability. The proposed control structure provides a hybrid behaviour between two CACC controllers with different time gaps according to the string position of the vehicle with the V2V communication link available. An stable hybrid behavior between both controllers is ensured through the Youla-Kucera parameterization. Simulation and real experiments show the proper behaviour of the designed control algorithm and a good performance compared to existing ACC/CACC controllers

Optimal speed trajectory and energy management control for connected and automated vehicles

Connected and automated vehicles (CAVs) emerge as a promising solution to improve urban mobility, safety, energy efficiency, and passenger comfort with the development of communication technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). This thesis proposes several control approaches for CAVs with electric powertrains, including hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs), with the main objective to improve energy efficiency by optimising vehicle speed trajectory and energy management system. By types of vehicle control, these methods can be categorised into three main scenarios, optimal energy management for a single CAV (single-vehicle), energy-optimal strategy for the vehicle following scenario (two-vehicle), and optimal autonomous intersection management for CAVs (multiple-vehicle). The first part of this thesis is devoted to the optimal energy management for a single automated series HEV with consideration of engine start-stop system (SSS) under battery charge sustaining operation. A heuristic hysteresis power threshold strategy (HPTS) is proposed to optimise the fuel economy of an HEV with SSS and extra penalty fuel for engine restarts. By a systematic tuning process, the overall control performance of HPTS can be fully optimised for different vehicle parameters and driving cycles. In the second part, two energy-optimal control strategies via a model predictive control (MPC) framework are proposed for the vehicle following problem. To forecast the behaviour of the preceding vehicle, a neural network predictor is utilised and incorporated into a nonlinear MPC method, of which the fuel and computational efficiencies are verified to be effective through comparisons of numerical examples between a practical adaptive cruise control strategy and an impractical optimal control method. A robust MPC (RMPC) via linear matrix inequality (LMI) is also utilised to deal with the uncertainties existing in V2V communication and modelling errors. By conservative relaxation and approximation, the RMPC problem is formulated as a convex semi-definite program, and the simulation results prove the robustness of the RMPC and the rapid computational efficiency resorting to the convex optimisation. The final part focuses on the centralised and decentralised control frameworks at signal-free intersections, where the energy consumption and the crossing time of a group of CAVs are minimised. Their crossing order and velocity trajectories are optimised by convex second-order cone programs in a hierarchical scheme subject to safety constraints. It is shown that the centralised strategy with consideration of turning manoeuvres is effective and outperforms a benchmark solution invoking the widely used first-in-first-out policy. On the other hand, the decentralised method is proposed to further improve computational efficiency and enhance the system robustness via a tube-based RMPC. The numerical examples of both frameworks highlight the importance of examining the trade-off between energy consumption and travel time, as small compromises in travel time could produce significant energy savings.Open Acces