Model Predictive Control based Stability Control of Autonomous Vehicles on Low Friction Road

Joa, Eunhyek; Hyun, Y.; Park, Kwanwoo; Kim, Jayu; Yi, Kyongsu

doi:10.1109/iv47402.2020.9304622

Cited by 2 publications

(1 citation statement)

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“…In [35], a linear MPC-based stability control system was proposed for low-friction roads. The authors developed a controller for both the longitudinal and lateral controls and demonstrated that their controller worked well even without knowing the value of the friction coefficient.…”

Section: Adverse Weather Conditionsmentioning

confidence: 99%

Autonomous Vehicle Decision-Making and Control in Complex and Unconventional Scenarios—A Review

2023

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The development of autonomous vehicles (AVs) is becoming increasingly important as the need for reliable and safe transportation grows. However, in order to achieve level 5 autonomy, it is crucial that such AVs can navigate through complex and unconventional scenarios. It has been observed that currently deployed AVs, like human drivers, struggle the most in cases of adverse weather conditions, unsignalized intersections, crosswalks, roundabouts, and near-accident scenarios. This review paper provides a comprehensive overview of the various navigation methodologies used in handling these situations. The paper discusses both traditional planning methods such as graph-based approaches and emerging solutions including machine-learning based approaches and other advanced decision-making and control techniques. The benefits and drawbacks of previous studies in this area are discussed in detail and it is identified that the biggest shortcomings and challenges are benchmarking, ensuring interpretability, incorporating safety as well as road user interactions, and unrealistic simplifications such as the availability of accurate and perfect perception information. Some suggestions to tackle these challenges are also presented.

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Section: Adverse Weather Conditionsmentioning

confidence: 99%