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

Postural and Muscular Responses of Car Occupants under Pre-Crash Conditions

Advanced integrated safety technologies in modern cars such as collision avoidance intervention and pre-crash activated restraint systems require comprehensive research on how vehicle occupants respond to these systems. The aim of this thesis is to provide insights into car passengers’ body kinematics and muscle activations in representative pre-crash circumstances with respect to two belt configurations (i.e., standard versus pre-pretensioner). Another objective is to explore the influence of occupants’ individual characteristics — namely age, stature, and sex — on their body kinematics. A complementary objective is to provide validation data for human body models (HBMs).A set of in-vehicle experiments was carried out in which front-row passengers were traveling at 73 km/h and subjected to autonomous lane changes and lane changes combined with braking, each with two belt configurations: standard and reversible pre-pretensioner belts. Volunteer muscle activations were measured by the surface electromyography (EMG) technique. Transformation of coordinates corresponding to several film targets attached to the head and upper torso was used to calculate the kinematics in 3-D. The volunteers’ EMG and kinematics were processed, and the quantified kinematics were statistically explored using principal component analysis and linear mixed model.Compared to the standard belt, pre-tensioning the seat belt prior to the maneuvers reduced lateral and forward displacements of the head and upper torso significantly. Seat belt pre-tensioning was also associated with earlier muscle activation onset and significantly lower activation amplitude for specific muscles. The influence of sex, stature, and their interaction on the head and upper torso kinematics were found statistically significant but accounted for a small amount of variance. A statistical model was developed which can predict head and upper torso kinematics of occupants with different stature and sex. The data provided in this thesis can be used for further enhancement and validation of HBMs. Consequently, the design of integrated safety systems in modern cars can benefit from more biofidelic models representing a wide range of population more accurately. Further statistical investigations for other types of omnidirectional loading scenarios and, preferably with a larger and more diverse sample space, are required to establish more accurate statistical models that can be generalized to the whole population

Preliminary results on correct-by-construction control software synthesis for adaptive cruise control

Abstract-A plethora of driver convenience and safety automation systems are being introduced into production vehicles, such as electronic stability control, adaptive cruise control, lane keeping, and obstacle avoidance. Assuring the seamless and safe integration of each new automation function with existing control functions is a major challenge for vehicle manufacturers. This challenge is compounded by having different suppliers providing software modules for different control functionalities. In this paper, we report on our preliminary steps to address this problem through a fresh perspective combining formal methods, control theory, and correct-by-construction software synthesis. In particular, we begin the process of synthesizing the control software module for adaptive cruise control from formal specifications given in Linear Temporal Logic. In the longer run, we will endow each interacting software module with an assume-guarantee specification stating under which environment assumptions the module is guaranteed to meet its specifications. These assume-guarantee specifications will then be used to formally prove correctness of the cyber-physical system obtained where the integrated modules interact with the physical dynamics

LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving

Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise cognitive pathways to enable comprehensive reasoning with LLMs, and develop algorithms for translating LLM decisions into actionable driving commands. Through this approach, LLM decisions are seamlessly integrated with low-level controllers by guided parameter matrix adaptation. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination, thanks to the commonsense reasoning capabilities of LLMs. This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability. We aspire for it to serve as inspiration for future research in this field. Project page: https://sites.google.com/view/llm-mp

Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks