# Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data

arXiv: https://arxiv.org/abs/2208.00249 · HTML: https://mlsanity.com/paper/2208.00249/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 83 references that are in the map.
- First posted on arXiv 30 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network](https://mlsanity.com/paper/2005.07796/) (2020)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability](https://mlsanity.com/paper/2012.13603/) (2020)
- [MIMO-SAR: A Hierarchical High-resolution Imaging Algorithm for mmWave FMCW Radar in Autonomous Driving](https://mlsanity.com/paper/2101.09293/) (2021)
- [CARPAL: Confidence-Aware Intent Recognition for Parallel Autonomy](https://mlsanity.com/paper/2003.08003/) (2020)
- [An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving](https://mlsanity.com/paper/2112.04095/) (2021)
- [Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline](https://mlsanity.com/paper/2111.03511/) (2021)
- [Fusion of Sensors Data in Automotive Radar Systems: A Spectral Estimation Approach](https://mlsanity.com/paper/1908.02504/) (2019)

## Abstract (arXiv)

Advanced driver assistance systems (ADAS) are designed to improve vehicle safety. However, it is difficult to achieve such benefits without understanding the causes and limitations of the current ADAS and their possible solutions. This study 1) investigated the limitations and solutions of ADAS through a literature review, 2) identified the causes and effects of ADAS through consumer complaints using natural language processing models, and 3) compared the major differences between the two. These two lines of research identified similar categories of ADAS causes, including human factors, environmental factors, and vehicle factors. However, academic research focused more on human factors of ADAS issues and proposed advanced algorithms to mitigate such issues while drivers complained more of vehicle factors of ADAS failures, which led to associated top consequences. The findings from these two sources tend to complement each other and provide important implications for the improvement of ADAS in the future.