Physics-inspired Neural Networks for Parameter Learning of Adaptive Cruise Control Systems

This paper proposes and develops a physics-inspired neural network (PiNN) for learning the parameters of commercially implemented adaptive cruise control (ACC) systems in automotive industry. To emulate the core functionality of stock ACC systems, which have proprietary control logic and undisclosed parameters, the constant time-headway policy (CTHP) is adopted. Leveraging the multi-layer artificial neural networks as universal approximators, the developed PiNN serves as a surrogate model for the longitudinal dynamics of ACC-engaged vehicles, efficiently learning the unknown parameters of the CTHP. The ability of the PiNN to infer the unknown ACC parameters is meticulous evaluated using both synthetic and high-fidelity empirical data of space-gap and relative velocity involving ACC-engaged vehicles in platoon formation. The results have demonstrated the superior predictive ability of the proposed PiNN in learning the unknown design parameters of stock ACC systems from different car manufacturers. The set of ACC model parameters obtained from the PiNN revealed that the stock ACC systems of the considered vehicles in three experimental campaigns are neither $L_2$ nor $L_\infty$ string stable.Comment: 11 pages, 8 figures, 3 tables, submitted to IEEE-T-V

Robust String Stability of Vehicle Platoons with Communication

This work investigates longitudinal spacing policies and vehicular communication strategies that can reduce inter-vehicular spacing between the vehicles of automated highway platoons, in the presence of parasitic actuation lags. Currently employed platooning technologies rely on the vehicle’s onboard sensors for information of the neighboring vehicles, due to this they may require large spacing between the vehicles to ensure string stability in the presence of uncertainties, such as parasitic actuation lags. More precisely, they require that the minimum employable time headway (hmin) must be lower bounded by 2τ₀ for string stability, where τ₀ is the maximum parasitic actuation lag. Recent studies have demonstrated that using vehicular communication one may be able to employ smaller spacing between vehicles while ensuring robustness to parasitic lags. However, precise results on the extent of such reduction are sparse in the literature. In this work, platoon string stability is used as a metric to study controllers that require vehicular communication, and find the amount of reduction in spacing such controllers can offer. First, the effects of multiple vehicle look ahead in vehicle platoons that employ a Constant Spacing Policy (CSP) based controller without lead vehicle information in the presence of parasitic lags is studied and string instability of such platoons is demonstrated. A robustly string stable CSP controller that employs information from the leader and the immediate predecessor is considered to determine an upper bound on the allowable parasitic lag; for this CSP controller, a design procedure for the selection of controller gains for a given parasitic lag is also provided. For a string of vehicles adopting a Constant Time Headway Policy (CTHP), it is demonstrated that the minimum employable time headway can be further decreased via vehicular communication in the following manner: (1) if the position, velocity and acceleration of the immediate predecessor vehicle is used, then the ii minimum employable time headway hmin can be reduced to τ₀; (2) if the position and velocity information of r immediately preceding vehicles is used, then hmin can be reduced to 4τ₀/(1 + r); (3) furthermore, if the acceleration of ‘r’ immediately preceding vehicles is used, then hmin can be reduced to 2τ₀/(1 + r); and (4) if the position, velocity and acceleration of the immediate and the r-th predecessors are used, then hmin = 2τ₀/(1 + r). Note that cases (3) and (4) provide the same lower bound on the minimum employable time headway; however, case (4) requires much less communicated information. Representative numerical simulations that are conducted to corroborate the above results are discussed. Vehicle formations employing ring structured communication strategies are also studied in this work and a combinatorial approach for developing ring graphs for vehicle formations is proposed. Stability properties of the platoons with ring graphs, limitations of using ring graphs in platoons, and methods to overcome such limitations are explored. In addition, with ring communication structure, it is possible to devise simple ways to recon- figure the graph when vehicles are added to or removed from the platoon or formation, which is also discussed in this work. Further, experimental results using mobile robots for platooning and two-dimensional formations using ring graphs are discussed

Model-based control for automotive applications

The number of distributed control systems in modern vehicles has increased exponentially over the past decades. Today’s performance improvements and innovations in the automotive industry are often resolved using embedded control systems. As a result, a modern vehicle can be regarded as a complex mechatronic system. However, control design for such systems, in practice, often comes down to time-consuming online tuning and calibration techniques, rather than a more systematic, model-based control design approach. The main goal of this thesis is to contribute to a corresponding paradigm shift, targeting the use of systematic, model-based control design approaches in practice. This implies the use of control-oriented modeling and the specification of corresponding performance requirements as a basis for the actual controller synthesis. Adopting a systematic, model-based control design approach, as opposed to pragmatic, online tuning and calibration techniques, is a prerequisite for the application of state-of-the-art controller synthesis methods. These methods enable to achieve guarantees regarding robustness, performance, stability, and optimality of the synthesized controller. Furthermore, from a practical point-of-view, it forms a basis for the reduction of tuning and calibration effort via automated controller synthesis, and fulfilling increasingly stringent performance demands. To demonstrate these opportunities, case studies are defined and executed. In all cases, actual implementation is pursued using test vehicles and a hardware-in-the-loop setup. • Case I: Judder-induced oscillations in the driveline are resolved using a robustly stable drive-off controller. The controller prevents the need for re-tuning if the dynamics of the system change due to wear. A hardware-in-the-loop setup, including actual sensor and actuator dynamics, is used for experimental validation. • Case II: A solution for variations in the closed-loop behavior of cruise control functionality is proposed, explicitly taking into account large variations in both the gear ratio and the vehicle loading of heavy duty vehicles. Experimental validation is done on a heavy duty vehicle, a DAF XF105 with and without a fully loaded trailer. • Case III: A systematic approach for the design of an adaptive cruise control is proposed. The resulting parameterized design enables intuitive tuning directly related to comfort and safety of the driving behavior and significantly reduces tuning effort. The design is validated on an Audi S8, performing on-the-road experiments. • Case IV: The design of a cooperative adaptive cruise control is presented, focusing on the feasibility of implementation. Correspondingly, a necessary and sufficient condition for string stability is derived. The design is experimentally tested using two Citroën C4’s, improving traffic throughput with respect to standard adaptive cruise control functionality, while guaranteeing string stability of the traffic flow. The case studies consider representative automotive control problems, in the sense that typical challenges are addressed, being variable operating conditions and global performance qualifiers. Based on the case studies, a generic classification of automotive control problems is derived, distinguishing problems at i) a full-vehicle level, ii) an in-vehicle level, and iii) a component level. The classification facilitates a characterization of automotive control problems on the basis of the required modeling and the specification of corresponding performance requirements. Full-vehicle level functionality focuses on the specification of desired vehicle behavior for the vehicle as a whole. Typically, the required modeling is limited, whereas the translation of global performance qualifiers into control-oriented performance requirements can be difficult. In-vehicle level functionality focuses on actual control of the (complex) vehicle dynamics. The modeling and the specification of performance requirements are typically influenced by a wide variety of operating conditions. Furthermore, the case studies represent practical application examples that are specifically suitable to apply a specific set of state-of-the-art controller synthesis methods, being robust control, model predictive control, and gain scheduling or linear parameter varying control. The case studies show the applicability of these methods in practice. Nevertheless, the theoretical complexity of the methods typically translates into a high computational burden, while insight in the resulting controller decreases, complicating, for example, (online) fine-tuning of the controller. Accordingly, more efficient algorithms and dedicated tools are required to improve practical implementation of controller synthesis methods