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

Abstract

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.

Non-Conservative Trajectory Planning for Automated Vehicles by Estimating Intentions of Dynamic Obstacles

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Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

“…Currently, there are systems capable of improving the quality and experience of vehicle users; an example are the Advanced Driver-Assistance Systems (ADAS). These systems have gained considerable attention in the automotive field as enablers for vehicle energy consumption, safety, and comfort enhancement [ 1 ]. These systems exploit a large number of sensors available in some vehicles today and serve the driver, alerting him in cases of potential problems.…”

Emergency collision avoidance strategy for autonomous vehicles based on steering and differential braking

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