Modern navigation systems prioritize fuel efficiency and time savings, often overlooking the critical aspect of road safety. This study addresses this gap by developing an advanced safe route guidance approach for Electronic Route Guidance Systems that incorporates personalized safety metrics based on individual driving behaviors and road conditions. Using the Safety Performance Function to evaluate the overall risk of road segments, and a copula model to capture the correlation between driving behaviors and road risk, this research uses conditional probability theory to quantify the safety levels of road segments for drivers with different driving behaviors to tailor safer route recommendations. The proposed approach is validated using the crash data, road geometric data, and driving behavior data collected from Los Angeles, demonstrating its capability to customize navigation based on individual driving behavior and significantly enhance route safety. The findings suggest that the safest route may vary depending on driving behavior. This research not only incorporates individualized traffic safety in navigation but also offers a scalable framework for future navigation systems to incorporate safety as a fundamental component.

Forward collision warning (FCW) systems are available in many new vehicles and are becoming increasingly popular. It is important to clearly understand their effectiveness in reducing collision risks, as well as their potential negative impacts on driving safety and volatility. This research aims to answer this question using real-world connected vehicles (CV) data collected under naturalistic settings. We extract 2,332 FCW events from the New York City CV Pilot Deployment dataset, including 1,326 events in the treatment group (warning issued) and 1,006 in the control group (no warning issued). From these FCW events, eleven volatility and nine safety variables are proposed and calculated, considering the interactions between the following and leading vehicles. These variables are further filtered using an ensemble variable elimination and selection method based on Variance Inflation Factors, Person correlation, and Akaike Information Criterion. Three binary logit models are developed for modeling volatility, safety, and both volatility and safety, respectively. These models are compared based on their log-likelihood values and the margin effects (MEs) of variables. The results show that FCW systems significantly enhance driving safety by reducing the time vehicles spend in high-risk conditions (ME: −4.59%) and the period of harsh braking to avoid collisions (ME: −5.27%) through issuing audible warnings. Despite increases in driving volatility, the overall benefits of FCW on safety outweigh the risks, with a benefit-to-risk ratio of 48.72%, resulting in a favorable net effect on driving dynamics from a statistical perspective.

Human-driven Vehicles (HVs) and Autonomous Vehicles (AVs) will inevitably coexist in the future, resulting in mixed traffic conditions. Modeling car-following behavior in such mixed traffic is crucial for better understanding the differences in driving behavior between AVs and HVs, thereby facilitating the development of AV driving strategies to enhance traffic safety. In such mixed traffic, the car-following modes typically include AVs following HVs, HVs following HVs and HVs following AVs. Previous studies have compared the driving behavior of HVs following HVs and HVs following AVs, but few have considered the driving behavior of AVs following HVs in the comparison. To bridge this gap, this paper first decomposes car-following events in mixed traffic extracted from a Lyft level-5 dataset into driving primitives using the Hidden Markov Model with Gaussian Mixture Model (GMM-HMM). Then, the Latent Dirichlet Allocation with GMM (GMM-LDA) is introduced to cluster the driving primitives into different car-following behavior patterns. Based on the identified patterns, the differences across the three car-following modes are further explored. The results show that the driving behavior under three car-following modes differs from each other. Specifically, compared to the HVs following HVs mode, in the AVs following HVs mode, AVs tend to maintain car-following behavior patterns with a longer distance gap and adopt a lower speed to ensure safety. This study offers a comprehensive understanding of car-following behavior in mixed traffic, which will facilitate the development of human-like driving strategies for AVs and enhance the safety of mixed traffic.

Route selection can greatly affect vehicle fuel consumption and emissions. Finding the most fuel/energy-efficient route is known as the eco-routing problem. Existing eco-routing solutions do not effectively consider the critical traffic signal information and rely on fuel consumption models that may not be sufficiently accurate. To address the eco-routing problem in a signalized traffic network, this paper proposes a graph convolutional network based multi-objective meta-deep Q-learning (GM$^{\bm{2}}$DQL) method. The problem is formulated as dynamic multi-objective Markov decision processes (MOMDP) and is tackled through deep reinforcement learning and meta-learning. We identify that graph convolutional network (GCN) is an efficient and suitable feature representation for a signalized traffic network. GM$^{\bm{2}}$DQL can explore the optimal routes with respect to drivers’ different preferences on saving fuel and travel time. Through GM$^{\bm{2}}$DQL, the agent is trained under a series of learning environments that are characterized by historical vehicle trajectories, fuel consumption data, and traffic signal data in the remote data center. The vehicle requesting eco-routing service can download the model that represents the action value function of the historical dynamic driving conditions. The model in the vehicle can quickly adapt to the most recent driving condition through online one-shot learning and predict the optimal eco-routes for the subsequent unseen driving conditions of the signalized traffic network. Extensive proof-of-concept experiments validate that GM$^{\bm{2}}$DQL can effectively discover optimal eco-routes. It saves up to 71% travel time and 62% fuel, compared to the conventional shortest-path routing strategy that is widely used in navigation systems.

In this paper we provide a proof of principle of a new method for addressing the ethics of autonomous vehicles (AVs), theData-Theories Method, in which vehicle crash data is combined with philosophical ethical theory to provide a guide to action for AV algorithm design. We use this method to model three scenarios in which an AV is exposed to risk on the road, and determine possible actions for the AV. We then examine how different philosophical perspectives on agent partiality, or the degree to which one can act in one’s own self-interest, might address each scenario. This method shows why modelling the ethics of AVs using data is essential. First, AVs may sometimes have options that human drivers do not, and designing AVs to mimic the most ethical human driver would not ensure that they do the right thing. Second, while ethical theories can often disagree about what should be done, disagreement can be reduced and compromises found with a more complete understanding of the AV’s choices and their consequences. Finally, framing problems around thought experiments may elicit preferences that are divergent with what individuals might prefer once they are provided with information about the real risks for a scenario. Our method provides a principled and empirical approach to productively address these problems and offers guidance on AV algorithm design.

Cracks are important indicators of pavement structural and operational conditions. Early pavement crack detection and treatments can help extend pavement service life, reduce fuel consumption, and improve safety and ride quality. Pavement distress surveys have traditionally been performed manually by visually inspecting the roads, which is labor-intensive and time-consuming. Therefore, computer-vision-based automated crack detection has great practical significance in pavement maintenance and traffic safety. Traditional image processing techniques are sensitive to noise in images and are thus likely to miss detecting some cracks due to the crack texture variety, complex lighting conditions, and various similar but irrelevant objects on the road. This paper adopts and enhances DeepLabv3+, a popular deep learning framework for semantic image segmentation, for road pavement crack detection. We propose a multi-scale attention module in the decoder of DeepLabv3+ to generate an attention mask and dynamically assign weights between high-level and low-level feature maps. Compared with fixed weights across different features, the dynamic weights strategy can assign more reasonable weights to different feature maps. Ablation experiments show that the attention mask can effectively help the model better combine multi-scale features and generate more accurate pavement crack segmentation results. The proposed method achieves state-of-the-art results on three benchmarks, including Crack500, DeepCrack, and FMA (Fitchburg Municipal Airport) datasets. We further test it on pavement crack images captured by smartphones, and the results show that it provides a viable approach to road pavement crack segmentation in practice with excellent performance.

Driver behaviors, particularly lane-changing behaviors, have an important effect on the safety and throughput of the roadway-vehicle-based transportation system. Lane-changing models are a vital component of various microscopic traffic simulation tools, which are extensively used and playing an increasingly important role in Intelligent Transportation Systems studies. The authors conducted a detailed review and systematic comparison of existing microscopic lane-changing models that are related to roadway traffic simulation to provide a better understanding of respective properties, including strengths and weaknesses of the lane-changing models, and to identify potential for model improvement using existing and emerging data collection technologies. Many models have been developed in the last few decades to capture the uncertainty in lane change modeling; however, lane-changing behavior in the real world is very complex due to driver distraction (e.g., texting and cellphone or smartphone use) and environmental (e.g., pavement and lighting conditions) and geometric (e.g., horizontal and vertical curves) factors of the roadway, which have not been adequately considered in existing models. Therefore, large and detailed microscopic vehicle trajectory data sets are needed to develop new lane changing models that address these issues, and to calibrate and validate lane-changing models for representing the real world reliably. Possible measures to improve the accuracy and reliability of lane-changing models are also discussed in this paper.