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Survey of Advanced Suspension Developments and Related Optimal Control Applications11This paper was not presented at any IFAC meeting. This paper was recommended for publication in revised form by Editor Karl Johan Åström.,22Simple, mostly LQ-based optimal control concepts gave useful insight…
Exploring microscopic driving volatility in naturalistic driving environment prior to involvement in safety critical events—Concept of event-based driving volatility
Development of a framework for risky driving scenario identification, individual risk assessment, and group risk differences estimation using naturalistic driving data from the i-DREAMS project
Examining collision avoidance behavior of distracted drivers: A correlated grouped random parameters accelerated failure time model with heterogeneity-in-means
How does the driver respond to rear-end risk under time-varying driving styles: A temporal instability test using the model with heterogeneity in means and variances
Development of a bus real-time crash risk prediction framework by using a self-attention-based bidirectional long and short-term memory network with anomaly detection learning and mixed sequence features
Bayesian extreme value analysis of kinematic-based surrogate measure of safety to detect crash-prone conditions in connected vehicles environment: A driving simulator experiment
Modeling the impact of driving aggression on lane change performance Measures: Steering compensatory Behavior, lane change execution duration and crash probability
Evaluating Effects of Cognitive Load, Takeover Request Lead Time, and Traffic Density on Drivers’ Takeover Performance in Conditionally Automated Driving
The 2nd Workshop on Localization vs. Internationalization: Impact of COVID-19 Pandemic on AutomotiveUI Activities from the View of Diversity and Inclusion
Multi-Evidence Accumulation Decision Framework: Understanding Human Lane-change Behavior Mechanistic through a Cognitively Inspired Safety–Efficiency Approach
Can Digital Road Safety Campaigns Nudge Sustained Safer Riding? Evidence from Behavioural Segmentation of Two-Wheeler Riders using Naturalistic Riding Data
Lane-Change Risk When the Subject Vehicle Is Faster Than the Following Vehicle: A Case Study on the Lane-Changing Warning Model Considering Different Driving Styles
Big Data-Based Driving Pattern Clustering and Evaluation in Combination with Driving Circumstances
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