首页|Sight distance analysis of vehicles with driving automation on horizontal curves of as-built highway tunnels
Sight distance analysis of vehicles with driving automation on horizontal curves of as-built highway tunnels
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NETL
NSTL
Elsevier
Compared to open roads, highway tunnels' continuous semi-enclosed wall structures exacerbate sight distance challenges for vehicles equipped with automation systems (AV) in curved segments. However, AV's adaptability to existing road geometry in tunnels, which was primarily tailored for traditional human-driven vehicles, remains inconclusive. Therefore, this study aims to investigate the influence of horizontal curved-tunnel geometry on AV's available sight distance (ASD) and analyze AV's sight distance safety (SDS). To this end, we established a virtual co-simulation platform for emulating AV's ASD in diverse tunnel scenarios, which include light detection and ranging (LiDAR)-based sensing configurations and tunnel geometry combinations. On this basis, the important features related to ASD were extracted using the recursive feature elimination algorithm and several widely-used machine-learning models were developed to predict ASD estimation. The Shapley additive explanation analysis was conducted on the most performant model to interpret the feature effects. Moreover, reliability analyses under varying driving automation levels, speeds, and pavement conditions were conducted to quantify the probability of noncompliance with SDS in scenarios focusing on circular curves, followed by proposing an SDS evaluation framework. The results show that: i) random forest model outperforms other machine-learning models in predicting ASD estimation; ii) higher-type tunnel geometry, higher-end sensing configurations, driving on the outside curve lane, and higher mounting height of LiDAR achieve longer ASD; iii) lower-end sensing configurations cause ASD to be less sensitive to the tunnel geometry; iv) SDS deteriorates in the orders of moist, dry, and wet pavement conditions and automation levels 4, 3, 1, and 2; v) AV adapt to low-type highway tunnels' horizontal curves from the sight distance perspective, but may fail in high-type designs. These findings shed light on the impact mechanism of tunnel curves on AV's ASD and serve to improve AV's road-oriented operational design domain and identify the tunnel segment that is significantly non-compliant with SDS.
NETL
NSTL
Elsevier
