Autonomous driving paper index
Predictive Analytics and Consumer Buying Behaviour for Personal-Use Four-Wheelers: A Quantitative Study of Eastern Maharashtra
One-line summary
The Indian automobile market has grown increasingly complex as rising incomes, urbanization, and digital information access reshape how consumers evaluate personal-use four-wheelers.
Engineering notes
Key topics: autonomous driving, perception. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
The Indian automobile market has grown increasingly complex as rising incomes, urbanization, and digital information access reshape how consumers evaluate personal-use four-wheelers. This study applies predictive analytics to model consumer buying behaviour in Eastern Maharashtra (Vidarbha region), an under-studied semi-urban market. Using a cross-sectional survey of 384 respondents selected through simple random sampling and Cochran's (1977) formula, three behavioural constructs — economic value, brand perception, and service assurance — were measured on five-point Likert scales and analysed through exploratory factor analysis (EFA), multiple linear regression, logistic regression, and a decision-tree classifier. The three-factor structure explained 60.2% of common variance (KMO = .80; Bartlett's χ² = 1315.06, p < .001), and reliability exceeded Nunnally and Bernstein's (1994) threshold for all constructs (α = .81–.83). The regression model explained 58.2% of variance in purchase-intention scores (R² = .582, F(3, 380) = 176.69, p < .001), with brand perception (β = .428) emerging as the strongest predictor, followed by economic value (β = .391) and service assurance (β = .196). The logistic model classified purchase likelihood with 73.4% accuracy (AUC = .816), outperforming the decision-tree classifier (63.8% accuracy, AUC = .683). Findings indicate that brand-related perceptions have overtaken purely economic considerations as the dominant driver of automobile purchase intention in this regional market, offering manufacturers a validated, data-driven framework for demand forecasting and segmentation.
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