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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Role of Risk Perception in Predicting the Intentions of Wakeful and Drowsy Drivers</title>
      <link>https://trid.trb.org/View/2775981</link>
      <description><![CDATA[Driver intention recognition algorithms play a crucial role in understanding driver–vehicle interactions. In highly automated vehicles, drivers are occasionally instructed to take control. Driver–vehicle interaction is, therefore, an important research topic. This paper uses risk levels and risk dynamics to provide a ground for developing intention recognition algorithms. It also investigates the effect of drowsiness on a driver’s ability to perform defensive actions. As human controllers are primarily event-based, the driver is modeled as an event-based controller that monitors risk-based signals and interacts with the vehicle pedals. A Gaussian support vector machine (SVM) model was used to predict the reaction type. A car-following experiment was conducted, in wakeful and drowsy phases, to assess wakeful driver actions and the ability of drowsy drivers to confine risk-based variables. Twenty-one drivers were tested in a wakeful state. The results achieved maximum and minimum accuracies of 81.9% and 60.8% in predicting the type of driver’s defensive action. The rate of the accelerator pedal release was affected by the risk and its time derivative, while the duration of switching from the accelerator to the brake was only affected by the time derivative of risk. Subsequently, the effects of drowsiness on driver performance were explored. Finally, a long short-term memory (LSTM) model was employed to predict drivers’ actions. The findings suggest that monitoring drivers’ risk estimations and their defensive pedal responses reveal patterns for predicting both the timing and the type of driver reactions.]]></description>
      <pubDate>Fri, 11 Sep 2026 08:43:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775981</guid>
    </item>
    <item>
      <title>Intelligent defensive driving for autonomous vehicles: Framework, strategy and verification</title>
      <link>https://trid.trb.org/View/2644106</link>
      <description><![CDATA[As autonomous driving advances to higher levels, conventional decision-making algorithms for autonomous vehicles (AVs) often inadequately address long-tail issues composed of low-frequency, high-uncertainty, and extreme scenarios, leading to recurrent safety incidents. How to leverage humans’ excellent defensive driving experience refined through expert knowledge to upgrade autonomous driving to intelligent defensive driving, thereby systematically improving driving safety serves as the core objective of this paper. To tackle this challenge, this paper proposes an integrated research scheme for the intelligent defensive driving of AVs. Firstly, an overall framework for intelligent defensive driving of AVs is constructed, which includes a systematic classification method for defensive driving scenarios and a hierarchical design process for defensive driving. Secondly, an online defensive driving monitoring mechanism for AVs is designed based on experience-triggered conditions, and a safety decision-making strategy is developed integrating formalized defensive driving experience. Finally, intelligent defensive driving performance for AVs is verified based on the “perception insufficiency” classification. The simulation results show that the proposed research scheme can utilize the defensive driving trigger mechanism to anticipate potential risks in multiple risk scenarios, improving the average values of the minimum relative braking distance, minimum time-to-collision and advance braking time by 7.49 m, 1.56 s, and 2.84 s, respectively, while significantly reducing the probability of emergency accidents and maintaining system robustness and real-time computational performance.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644106</guid>
    </item>
    <item>
      <title>An Adapted Evasive Manoeuvre Assist Function for Over-Reactive and Under-Reactive Drivers</title>
      <link>https://trid.trb.org/View/1973220</link>
      <description><![CDATA[In the present paper, an Evasive Manoeuvre Assist (EMA) function is designed to adapt to different types of drivers, by an optimised steering torque overlay. The existing EMA function amplifies the driver steering inputs using a feed-forward controller which might not necessarily help an over-reactive driver. There exists a need for an EMA which adapts to different drivers as it minimises the risk of collision and gives the driver an experience of good control. The focus of this paper is to identify and define a proper steering sequence reference model for closed-loop feedback control design. A simple single-point preview model is designed first to calculate the reference steering angle. A few test scenarios are set-up using the IPG CarMakersimulation tool. The reference model is then calibrated with respect to the amplitude and frequency of the steering sequence by offline optimisation to obtain the optimal steering profile. A feedback controller is then designed using this reference model. The robustness of the function is verified in real-time, using a Volvo rapid-prototype test vehicle.]]></description>
      <pubDate>Fri, 15 Dec 2023 08:45:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1973220</guid>
    </item>
    <item>
      <title>A Novel Driver Warning System with Hedging to Promote Defensive Driving</title>
      <link>https://trid.trb.org/View/2250683</link>
      <description><![CDATA[This project aims to improve current in-vehicle driver warning systems that only alert drivers in the host vehicles by developing a new driving warning system that hedges against the remaining safety risk of the current driver warning systems and promotes defensive driving of the surrounding vehicles. Specifically, two new driver warning algorithms, namely FCW-H: Forward Collision Warning with Hedging and BSW-H: Blind Spot Warning with Hedging, will be developed. To evaluate the proposed algorithms, a driving simulator will be used and driving data will be collected and analyzed. The results of this project have the potential to reduce traffic crashes and leave a tangible impact in preparing for the next-generation intelligent transportation system technologies in the era of smart cities.]]></description>
      <pubDate>Thu, 21 Sep 2023 12:41:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2250683</guid>
    </item>
    <item>
      <title>Exploring hazard anticipation and stress while driving in light of defensive behavior theory</title>
      <link>https://trid.trb.org/View/2186152</link>
      <description><![CDATA[In driving, poor hazard anticipation would provide drivers less time to prepare an appropriate response, increasing the urgency of the situation and generating more stress. Assuming this, the current study seeks to determine whether a predictable road hazard triggers hazard anticipation in drivers that can mitigate the ensuing stress response, and whether the stress response is influenced by driving experience. In a simulated road environment, a cue was used to trigger hazard anticipation, and a road hazard to induce a stress response. Heart rate, pupil diameter, driving speed, subjective stress, arousal, and negative emotions, were retrieved from 36 drivers who all faced the cue followed by the hazard (i.e., a predictable hazard), the cue only, and the hazard only. In the light of work on defensive behaviors, the findings indicate that a predictable hazard triggers hazard anticipation detectable via (1) freezing behavior-characterized by cardiac deceleration-(2) anticipatory pupil dilation and (3) anticipatory speed deceleration. The results also point to a beneficial role for hazard anticipation in reducing driver stress, as evidenced by reductions in peak heart rate levels, as well as in reported levels of stress and negative emotions. Finally, the findings showed an influence of driving experience on reported levels of stress. Overall, this study shows how previous work on defensive behaviors can be used to gain insight into the processes and driving behaviors involved in hazard anticipation and stress.]]></description>
      <pubDate>Thu, 27 Jul 2023 16:55:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2186152</guid>
    </item>
    <item>
      <title>Exploring the factors that affect the defensive driving behavior of bus drivers: the application of TPB and PMT theories</title>
      <link>https://trid.trb.org/View/2146379</link>
      <description><![CDATA[As early as 1948, defensive driving was accurately defined. Defensive driving was first promoted among professional drivers, and defensive driving training is still an important part of the regular training of professional drivers. The level of defensive driving is one of the standards for professional drivers (such as bus drivers). However, there is still a lack of quantitative evaluation methods for the defensive driving level of bus drivers. To fill this gap, the main purpose of this study was to compile a scale for assessing the defensive driving level of bus drivers, and to explore the influencing factors of the defensive driving behavior intention in bus drivers. Based on the definition of defensive driving, a defensive driving scale was compiled. Furthermore, a defensive driving intention scale was designed based on the theory of planned behavior and protective motivation theory to determine the factors affecting the defensive driving intention. A questionnaire survey was conducted among 312 bus drivers in a Chinese city. The results showed that the bus drivers generally reported a high defensive driving level and behavior intention. In addition, the results of the integrated model showed that attitude (β = 0.18) and utility susceptibility (β = 0.69) were positively correlated with behavior intention, while the response cost (β = − 0.18) was negatively correlated with behavior intention.]]></description>
      <pubDate>Tue, 25 Apr 2023 09:49:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2146379</guid>
    </item>
    <item>
      <title>The Impact of Motor Vehicle Emergency Evasion and No Lights Crosswalks on Traffic Flow</title>
      <link>https://trid.trb.org/View/2019063</link>
      <description><![CDATA[This paper studies the influence of motor vehicle emergency avoidance behavior on traffic flow based on the improved NaSch model, focusing on the problem of human-vehicle conflicts caused by the implementation of compulsory yielding measures in a bad right-of-way environment. The authors use George2.1 software to extract running data of motor vehicles under the pedestrian crossing without traffic lights in urban areas and analyze the three movement conditions of motor vehicles: directly passing, slowing down and avoiding, and emergency avoiding. Based on the MATLAB software, an improved NaSch model was constructed to simulate the emergency avoidance behavior of motor vehicles on a single lane, and the impact of the emergency avoidance behavior of motor vehicles on traffic flow under the variables of pedestrian crossing probability, maximum speed of motor vehicles, and motor vehicle density was studied. The results show that the emergency avoidance behavior of motor vehicles has a greater impact on the safety of passengers and pedestrians and the order of road traffic. The increase in the probability of pedestrians crossing the street and the limitation of the maximum speed of the road will both have an effect on the traffic flow.]]></description>
      <pubDate>Thu, 17 Nov 2022 10:15:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2019063</guid>
    </item>
    <item>
      <title>One-dimensional cellular automaton traffic flow model based on defensive driving strategy</title>
      <link>https://trid.trb.org/View/1907409</link>
      <description><![CDATA[In a SDNaSch model, vehicles tend to brake sharply, leading to an inherent safety risk cellular automata based on defensive driving, considering the deceleration behavior of the preceding vehicle before the braking process. A numerical simulation is performed to determine the flow–density relationship, average speed–density relationship, emergency brake ratio–density relationship and space–time diagram under different defensive deceleration probabilities, and the safety and stability of the defensive driving model are analyzed. The results show that the use of the defensive driving model improves the traffic flow in the medium–high density area, delays the occurrence of static congestion, considerably reduces the emergency braking behavior of operating vehicles, and improves the stability and safety during transportation. In the medium–high density area, a stable and uniform synchronized flow appears, and the “speed transition” phenomenon consistent with the measured data is observed simultaneously. Compared with the traditional SDNaSch model, the proposed model can better describe the actual running state of the traffic flow.]]></description>
      <pubDate>Tue, 22 Feb 2022 10:27:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/1907409</guid>
    </item>
    <item>
      <title>A Recognition Method of Aggressive Driving Behavior Based on Ensemble Learning</title>
      <link>https://trid.trb.org/View/1905232</link>
      <description><![CDATA[Aggressive driving behavior (ADB) is one of the main causes of traffic accidents. The accurate recognition of ADB is the premise to timely and effectively conduct warning or intervention to the driver. There are some disadvantages, such as high miss rate and low accuracy, in the previous data-driven recognition methods of ADB, which are caused by the problems such as the improper processing of the dataset with imbalanced class distribution and one single classifier utilized. Aiming to deal with these disadvantages, an ensemble learning-based recognition method of ADB is proposed in this paper. First, the majority class in the dataset is grouped employing the self-organizing map (SOM) and then are combined with the minority class to construct multiple class balance datasets. Second, three deep learning methods, including convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU), are employed to build the base classifiers for the class balance datasets. Finally, the ensemble classifiers are combined by the base classifiers according to 10 different rules, and then trained and verified using a multi-source naturalistic driving dataset acquired by the integrated experiment vehicle. The results suggest that in terms of the recognition of ADB, the ensemble learning method proposed in this research achieves better performance in accuracy, recall, and F1-score than the aforementioned typical deep learning methods. Among the ensemble classifiers, the one based on the LSTM and the Product Rule has the optimal performance, and the other one based on the LSTM and the Sum Rule has the suboptimal performance.]]></description>
      <pubDate>Mon, 24 Jan 2022 17:24:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/1905232</guid>
    </item>
    <item>
      <title>Reducing Elevated Gravitational-Force Events through visual feedback: a simulator study</title>
      <link>https://trid.trb.org/View/1767608</link>
      <description><![CDATA[Road traffic accidents represent one of the leading causes of death across all age groups globally. Most of these accidents can be directly attributed to drivers’ failure to select the correct driving speed. Thus, actions aimed to mitigate inappropriate driving performance, including speeding, are needed. Here, the authors used a dynamic driving simulator to investigate the effects of different real-time coaching programs on driving performance, specifically on the occurrence of Elevated Gravitational-Force Events (EGFEs). Forty-three drivers underwent a two-day evaluation. On the first day, participants –after an initial screening and depending on their driving style– were divided into two groups: defensive vs. aggressive drivers. On the second day, they received a different type of real time visual feedback based on their driving performance. For each of the two driving style groups, half of the drivers received contingent positive feedback (when smooth driving events occurred), the other half received contingent negative feedback (when harsh driving events occurred). Thus, there were four groups based on driving style and feedback. Overall, results showed that among aggressive drivers contingent feedback –independently from its type– reduces the occurrence of EGFEs. Potential applications of the proposed methodology include its use for Pay-how-you-drive programs aimed to improve driver speed control.]]></description>
      <pubDate>Fri, 19 Feb 2021 10:31:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/1767608</guid>
    </item>
    <item>
      <title>Investigating the Correlation between Driver’s Characteristics and Safety Performance</title>
      <link>https://trid.trb.org/View/1739854</link>
      <description><![CDATA[This research aims to correlate drivers’ characteristics with their safety performance. In order to achieve this objective, two different data sets were used deriving from 12 drivers who participated on an on-road driving experiment while being assessed by a safety behaviour expert. Drivers participating in the experiment also responded to the questionnaire administered, which mainly included self-evaluation questions along with others that concerned the condition of their vehicle, demographics, driving experience etc. The analysis of drivers’ behavior was carried out using the statistical methods of factor analysis and linear regression analysis. Three groups that characterize drivers’ perception of careless, aggressive and cautious driving behavior were derived from factor analysis. Moreover, linear regression analysis revealed that driving experience, headways, self-reported driving skill and defensive driving positively affect the overall on-road driving performance score. More precisely, it was ascertained that driving experience leads to statistically significant increase in overall on-road driving performance score. The findings of this research could be exploited for providing feedback on drivers regarding their driving behavior in order to further reduce their driving risk.]]></description>
      <pubDate>Sun, 01 Nov 2020 17:01:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/1739854</guid>
    </item>
    <item>
      <title>Multiple Linear Regression Model of Defensive Driving Technology and Driver Emotional Intelligence Improvement</title>
      <link>https://trid.trb.org/View/1724982</link>
      <description><![CDATA[In recent years, defensive driving technology has become an important research direction for road transport safety risk prevention and control. However, the research on defensive driving at home and abroad focuses on the feasibility, judgment, and recognition of defensive driving, and a quantitative research on the relationship between defensive driving training and driver emotional intelligence remains lacking. A multiple linear regression model to represent the relationship between defensive driving training and driver emotional intelligence improvement is proposed. This model is divided into two stages to describe the linear relationship between the five independent variables of driver psychology, physiology, emotion, driving intelligence, and learning time and the dependent variables of defensive driving training effectiveness. An F-test is carried out to verify the linear characteristics of the model. Results show that the average error probability of the linear impact of the five independent variables on the dependent variables is 0.047 in the two stages, which meets the requirements of multiple linear regression. The results prove that the proposed model is effective. Analysis of the model indicates that defensive driving training has two distinct characteristics on the improvement of driver emotional intelligence. In the initial stage, defensive driving training has the highest correlation with indicators related to driver agility. In the second stage, defensive driving training is highly correlated with indicators related to the psychological and emotional control of drivers.]]></description>
      <pubDate>Thu, 17 Sep 2020 17:54:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1724982</guid>
    </item>
    <item>
      <title>Evaluation of Driver Behavior at Railroad-Highway Grade Crossings Using Naturalistic Driving Study Data</title>
      <link>https://trid.trb.org/View/1716524</link>
      <description><![CDATA[The overall objective of this two-phase project is to investigate driver behavior at Highway-Railroad Grade Crossings (HRGCs) using two distinct, but complimentary techniques. Phase 1 of this project takes advantage of the extensive Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) database, collected over several years. The NDS study approach allows for systematic analysis of in-vehicle video and other sensors for direct observation of drivers during typical driving activities at HRGCs. The authors used the data together with an evaluation methodology developed at Michigan Tech as part of the project in an attempt to quantify the level of defensive driving behavior during HRGC traversals. More specifically, the analysis concentrated on: (1) driver response to different traffic control devices (TCDs) in place at HRGCs; (2) comparison of driver behavior at HRGCs with and without accidents between 2000 and 2010; and (3) exploration of the use of NDS data for trending analysis.  Phase 2 took advantage of the understanding developed in the first phase to create simulated scenarios that resemble environments found in the NDS data. Driver behavior data from these simulated scenarios was collected and compared with the NDS data sets. More specifically, the objectives were to: (1) select two HRGCs from the NDS dataset and recreate them in a simulated setting; (2) recruit student drivers to participate in a driving simulation study where drivers are exposed to different HRGCs; and (3) compare and contrast driver behavior between the NDS and simulator data sets, using the previously developed driver behavior score.]]></description>
      <pubDate>Mon, 13 Jul 2020 10:34:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1716524</guid>
    </item>
    <item>
      <title>Exploring automated vehicle driving styles as a source of trust information</title>
      <link>https://trid.trb.org/View/1646057</link>
      <description><![CDATA[Trust is important for users’ acceptance and adoption of automated vehicles (AVs). Previous research has mainly focused on what information affects user trust in AVs and how the user’s trust is affected by the way the content is communicated. However, recent studies have shown that trust may also be affected by the AV’s driving style. The aim of the study was to further investigate if and how the vehicle’s driving style affects user trust in AVs and, in particular, how this is expressed by users. An experiment involving 18 participants, using a Wizard of Oz setup and within a subject design, was conducted comparing two different driving styles, ‘Defensive’ and ‘Aggressive’. Trust was measured using a mixed method research design including momentaneous trust ratings and think-aloud procedures while driving, a post-run trust questionnaire as well as trust curve sketching and a personal interview. The results show that driving style had an effect on user trust and that the ‘Defensive’ driving style was perceived as more trustworthy, in part because it was deemed more predictable than the ‘Aggressive’ driving style. Furthermore, participants expressed trust in using affective, analogical and analytic responses, the two former during the test runs and the latter directly after each test run. The interview after the completion produced a more mixed result. By combining different data collection methods, a nuanced picture of the trust formation process and users’ trust in AVs was obtained. The study concludes that it is important to consider the vehicle performance information provided by the vehicle’s driving style so as to create user trust in AVs.]]></description>
      <pubDate>Thu, 19 Sep 2019 15:07:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/1646057</guid>
    </item>
    <item>
      <title>Influence of professional drivers’ personality traits on road traffic safety: case study</title>
      <link>https://trid.trb.org/View/1361299</link>
      <description><![CDATA[The purpose of this paper is to present basic elements of the research directed at identifying and determining the personality traits of professional drivers that affect safe, secure and enjoyable ride on public roads. A quantitative method has been used here, whereas data were acquired from a questionnaire based on a sample of 59 professional drivers. Determining personality traits of professional drivers that are in correlation with a safe and pleasant ride on the roads has been enabled by applying the five-factor model of personality (‘Big Five’) and the Personality Inventory NEO-PI. From these results it was concluded that safe operation of the vehicle in traffic involves the successful ‘conduct’ of oneself, which recognises the importance of certain personality traits of professional drivers for traffic safety and the need for appropriate professional selection in the case of employment of professional drivers. Research results implicate development of educational programmes aimed at achieving harmony of psychological, physical and sensory health, that is, programmes for permanent informing, educating and training professional drivers for defensive driving. The research opens the way for new research tasks that should help in creating a specific structure of curricula that can be used in a variety of transportation companies and enterprises to improve general and public safety.]]></description>
      <pubDate>Fri, 24 Jul 2015 12:10:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1361299</guid>
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