The world is undergoing a major demographic shift as older adults become a rapidly growing share of the population, creating new challenges for driving safety. In car-dependent regions such as the United States, driving remains essential for independence, access to services, and social participation. At the same time, aging can introduce gradual changes in vision, attention, reaction time, and driving control that quietly reduce safety. Today's assessment methods rely largely on infrequent clinic visits or simple screening tools, offering only a brief snapshot and failing to reflect how an older adult actually drives on the road. Our work starts from the observation that everyday driving provides a continuous record of functional ability and captures how a driver responds to traffic, navigates complex roads, and manages routine behavior. Leveraging this insight, we propose AURA, an Artificial Intelligence of Things (AIoT) framework for continuous, real-world assessment of driving safety among older adults. AURA integrates richer in-vehicle sensing, multi-scale behavioral modeling, and context-aware analysis to extract detailed indicators of driving performance from routine trips. It organizes fine-grained actions into longer behavioral trajectories and separates age-related performance changes from situational factors such as traffic, road design, or weather. By integrating sensing, modeling, and interpretation within a privacy-preserving edge architecture, AURA provides a foundation for proactive, individualized support that helps older adults drive safely. This paper outlines the design principles, challenges, and research opportunities needed to build reliable, real-world monitoring systems that promote safer aging behind the wheel.
Figures & tables
Figure 1. Overview of AURA
Dataset
Sample Size
Age Range
Time Span
Key Features
LongROAD
2402
65–80+
5 years
• Population Diversity • Trip-level driving logs • Time, mileage, and speed records
DRIVES
414
65–90+
3 months
• High-resolution GPS traces • Cognitive assessment data
CARLA Simulator
20 (10 seniors, 10 youth)
18–80+
One session
• High-frequency telemetry data • Full body tracking • Controlled driving scenarios
Table 1. Summary of Naturalistic and Controlled Driving Datasets
Figure 2. Overview of the CARLA simulator environment employed for controlled driving data collection.
Figure 3. Driving behavior is different between young and senior drivers.
Figure 4. Head movement with age difference.
Figure 5. Route-level safety score differences between senior and young drivers. Red points mark locations with maximal group divergence, while black points indicate baseline similarity. Senior drivers show widespread instability and reduced fluidity, with delayed reactions at intersections and larger route deviations in curves.
Figure 6. LongROAD dataset: different age-group driving patterns.
Figure 7. Senior weekly driving features.
Figure 8. PCA result for the driving behaviors.
Figure 9. Comparison of drivers ’ responses with and without a leading vehicle.
Figure 10. Driving under different weather conditions.
Figure 11. Safety Driving Importance Ratio
Grand Challenge
Observations
Vision Component
Design Directions
From Raw Data to Senior Safety
• High intra-driver variability • Inconsistent Behavior • Compensatory behaviors
Autonomous vehicles (AVs) have significantly advanced in real-world deployment in recent years, yet safety continues to be a critical barrier to widespread adoption. Traditional functional safety approaches, which primarily verify the reliability, robustness, and adequacy of AV hardware and software systems from a vehicle-centric perspective, do not sufficiently address the AV's broader interactions and behavioral impact on the surrounding traffic environment. To overcome this limitation, we propose a paradigm shift toward behavioral safety, a comprehensive approach focused on evaluating AV responses and interactions within the traffic environment. To systematically assess behavioral safety, we introduce a third-party AV safety assessment framework comprising two complementary evaluation components: the Behavioral Competency Test and the Driving Intelligence Test. The Behavioral Competency Test evaluates the AV's reactive behaviors under controlled scenarios, ensuring basic behavioral competency. In contrast, the Driving Intelligence Test assesses the AV's interactive behaviors within naturalistic traffic conditions, quantifying the frequency of safety-critical events to deliver statistically meaningful safety metrics before large-scale deployment. In Part II of this study, an open-source Level 4 Automated Driving System (ADS) is tested to demonstrate the effectiveness of the proposed method.
Henry X. Liu, Tinghan Wang, Xintao Yan +6
University of Michigan Transportation Research Institute, Ann Arbor, MI 48109 USA. · Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109 USA. · Department of Civil Engineering, The University of Hong Kong, Hong Kong 999077, China. +2
Road crashes remain a leading cause of preventable fatalities. Existing prediction models predominantly produce binary outcomes, which offer limited actionable insights for realtime driver feedback. These approaches often lack continuous risk quantification, interpretability, and explicit consideration of vulnerable road users (VRUs), such as pedestrians and cyclists. This research introduces SafeDriver-IQ, a framework that transforms binary crash classifiers into continuous 0-100 safety scores by combining national crash statistics with naturalistic driving data from autonomous vehicles. The framework fuses National Highway Traffic Safety Administration (NHTSA) crash records with Waymo Open Motion Dataset scenarios, engineers domain-informed features, and incorporates a calibration layer grounded in transportation safety literature. Evaluation across 15 complementary analyses indicates that the framework reliably differentiates high-risk from low-risk driving conditions with strong discriminative performance. Findings further reveal that 87% of crashes involve multiple co-occurring risk factors, with non-linear compounding effects that increase the risk to 4.5x baseline. SafeDriver-IQ delivers proactive, explainable safety intelligence relevant to advanced driver-assistance systems (ADAS), fleet management, and urban infrastructure planning. Beyond the specific application, the inverse modeling paradigm is domain-agnostic. Any binary risk classifier can be converted into a continuous, explainable safety-scoring system using the same pipeline without retraining. This framework shifts the focus from reactive crash counting to real-time risk prevention.
Joyjit Roy, Samaresh Kumar Singh, Sushanta Das +1
Independent Researcher · Austin, Texas, USA · Leander, Texas, USA +5
Although autonomous driving systems demonstrate high perception performance, they still face limitations when handling rare situations or complex road structures. Such road infrastructures are designed for human drivers, safety improvements are typically introduced only after accidents occur. This reactive approach poses a significant challenge for autonomous systems, which require proactive risk mitigation. To address this issue, we propose OD-RASE, a framework for enhancing the safety of autonomous driving systems by detecting road structures that cause traffic accidents and connecting these findings to infrastructure development. First, we formalize an ontology based on specialized domain knowledge of road traffic systems. In parallel, we generate infrastructure improvement proposals using a large-scale visual language model (LVLM) and use ontology-driven data filtering to enhance their reliability. This process automatically annotates improvement proposals on pre-accident road images, leading to the construction of a new dataset. Furthermore, we introduce the Baseline approach (OD-RASE model), which leverages LVLM and a diffusion model to produce both infrastructure improvement proposals and generated images of the improved road environment. Our experiments demonstrate that ontology-driven data filtering enables highly accurate prediction of accident-causing road structures and the corresponding improvement plans. We believe that this work contributes to the overall safety of traffic environments and marks an important step toward the broader adoption of autonomous driving systems.
Kota Shimomura, Masaki Nambata, Atsuya Ishikawa +4
Chubu University · Elith Inc. · Honda R&D Co., Ltd.