Safety improvements by intelligent connected vehicle technologies: A meta-analysis considering market penetration rates

Safety-oriented automated vehicle longitudinal control considering both stability and damping behavior

Extensive research has examined the potential benefits of Automated Vehicles (AVs) for increasing traffic capacity and improving safety. However, previous studies on AV longitudinal control have focused primarily on control stability and instability or tradeoffs between safety and stability, neglecting the importance of vehicle damping characteristics. This study aims to demonstrate the significance of explicitly considering safety in addition to stability in AV longitudinal control through damping behavior analysis. Specifically, it proposes a safety-oriented AV longitudinal control and provides recommendations on the control parameters. For the proposed AV control, an Adaptive Cruise Control (ACC) model is integrated with damping behavior analysis to model AV safety under continuous traffic perturbations. Numerical simulations are conducted to quantify the relationship between mobility and safety for AVs considering both damping behavior and control stability. Different ACC control parameters are evaluated in terms of damping and stability properties, and their safety impacts are assessed based on various surrogate safety measures such as Deceleration Rate to Avoid Crash (DRAC), Crash Potential Index (CPI) and Time-Integrated Time-to-collision (TIT). The results indicate that an underdamped state (ACC damping ratio < 1) is less safe than the critically damped state (ACC damping ratio = 1) and the overdamped state (ACC damping ratio > 1). Furthermore, given the same AV car-following time lag, ACC with a damping ratio between 1 and 1.2 provides better safety performance. Increasing the AV car-following time lag can improve both safety and stability when the remaining ACC control parameters are kept the same. In this case, the optimal safety-oriented ACC regions also increase. The findings of this study provide important insights into designing safe and stable AV longitudinal control algorithms.

Collision-avoidance lane change control method for enhancing safety for connected vehicle platoon in mixed traffic environment

In a mixed traffic environment, the connected vehicle platoon cannot communicate and collaborate with the surrounding vehicles. In this case, there is a high risk of collision in large vehicle platoon's lane change scenario where the non-connected surrounding vehicle occupies the target lane-changing space of the platoon. This study proposes a collision-avoidance lane change control method for a connected bus platoon to elude the non-connected vehicle in the target lane for completing lane change in the mixed traffic environment safely. A platoon vehicle sensor system with low-cost and low data processing complexity is designed, which equips with multiple sensors in longitudinal and lateral directions. Under control of the proposed platoon controller on the basis of vehicle-to-vehicle (V2V) communication, the platoon following vehicles are fully autonomous in both longitudinal and lateral directions. The safe lane change decision-maker is designed based on the Finite State Machine (FSM). The decision-maker fuses multiple sensor data and determines the lane change operation of the following vehicles. To verify the effectiveness of the proposed method, a three-vehicle platoon is carried out the lane change experiments in a high-fidelity mixed traffic scenario built by the PreScan-Simulink joint simulation platform. Exposure-to-Risk Index (ERI) of the platoon vehicles is adopted to evaluate the collision risk of the platoon during lane changing process. Three typical case scenarios are tested, including unimpeded lane change, passive waiting lane change, and active accelerating lane change. The simulation results show that all platoon vehicles have an excellent success rate in lane change without collision with the non-connected surrounding vehicle in these scenarios. The proposed method exhibits compelling benefits on improving the safety of platoon vehicles in the mixed traffic environment.

Application of explainable machine learning for real-time safety analysis toward a connected vehicle environment

Due to the difficulty of obtaining traffic flow data and conflicts simultaneously, conflict-based analysis using macroscopic traffic features is much less studied. This research aims to analyze real-time safety by a disaggregate study and explore the benefit of the connected vehicle (CV) for real-time safety evaluation. To avoid the endogeneity problem regarding conflicts and traffic features in regression models, machine learning is employed to obtain a reliable and practical real-time safety model. The results show that the Random Forest outperforms eXtreme Gradient Boosting, Support Vector Machine and Adaptive Boosting models, achieving the best performance with the highest AUC of 0.827. For a deep understanding of conflict mechanisms, the explainable machine learning method SHAP (SHapley Additive exPlanation) is introduced to improve the model interpretability providing insights into the impacts of traffic flow features. Lane difference regarding average speed is found to have the most significant impacts on real-time safety. Speed variation, the proportion of trucks and traffic volume are associated with conflict occurrence. Further analysis highlights that the impacts of traffic features are heterogeneous and there may exist specific patterns of paired features affecting real-time safety. Encouragingly, SHAP appears to be able to complement the traditional model with random components in terms of revealing heterogeneity. The explainable machine learning can also provide a solid basis for discretizing continuous variables while previous studies perform discretization mainly based on prior knowledge and experience. The experimental result regarding CV Market Penetration Rate (CV-MPR) demonstrates that the model performance is gradually elevated with the increase of penetration rate. The initial stage of the CV market (20%, 40% CV-MPR) yields the most significant gains in real-time safety evaluation. These findings can be used beneficially in active traffic management.

A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving

The evaluation of take-over performance and take-over safety performance is critical to improving the take-over performance of conditionally automated driving, and few studies have attempted to evaluate take-over safety performance. This study applied a binary logistic model to construct a take-over safety performance evaluation model. A take-over driving simulator was established, and a take-over simulation experiment was carried out. In the experiment, data were collected from 15 participants who took over the vehicle and performed emergency evasive maneuvers while performing non-driving-related task (NDRT). Then, to calibrate the abnormal trajectory, the Kalman filter is adopted to filter the disturbed vehicle positioning data and the belief rule-based (BRB) method is proposed to warn irregular driving behavior. The results revealed that the accident rate of male participants is higher than that of female participants in the three frequency take-over experiment, and the overall driving performance of female participants is higher than that of male participants. Meanwhile, medium and high take-over frequencies have a significant effect on the prevention of vehicle collisions. In the take-over safety performance evaluation model, the minimum time to collision (TTC) of 2.3 s is taken as the boundary between the dangerous group and the safety group, and the model prediction accuracy rate is 87.7%. In sum, this study enriches existing research on the safety performance evaluation of conditionally automated driving take-over and provides important implications for the design of driving simulators and the performance and safety evaluation of human-machine take-over.

A conceptual system dynamics model for cybersecurity assessment of connected and autonomous vehicles

Emerging Connected and Autonomous Vehicles (CAVs) technology have a ubiquitous communication framework. It poses security challenges in the form of cyber-attacks, prompting rigorous cybersecurity measures. There is a lack of knowledge on the anticipated cause-effect relationships and mechanisms of CAVs cybersecurity and the possible system behaviour, especially the unintended consequences. Therefore, this study aims to develop a conceptual System Dynamics (SD) model to analyse cybersecurity in the complex, uncertain deployment of CAVs. Specifically, the SD model integrates six critical avenues and maps their respective parameters that either trigger or mitigate cyber-attacks in the operation of CAVs using a systematic theoretical approach. These six avenues are: i) CAVs communication framework, ii) secured physical access, iii) human factors, iv) CAVs penetration, v) regulatory laws and policy framework, and iv) trust-across the CAVs-industry and among the public. Based on the conceptual model, various system archetypes are analysed. "Fixes that Fail", in which the upsurge in hacker capability is the unintended natural result of technology maturity, requires continuous efforts to combat it. The primary mitigation steps are human behaviour analysis, knowledge of motivations and characteristics of CAVs cyber-attackers, CAVs users and Original Equipment Manufacturers education. "Shifting the burden", where policymakers counter the perceived cyber threats of hackers by updating legislation that also reduces CAVs adaptation by imitations, indicated the need for calculated regulatory and policy intervention. The "limits to success" triggered by CAVs penetration increase the defended hacks to establish regulatory laws, improve trust, and develop more human analysis. However, it may also open up caveats for cyber-crimes and alert that CAVs deployment to be alignment with the intended goals for enhancing cybersecurity. The proposed model can support decision-making and training and stimulate the roadmap towards an optimized, self-regulating, and resilient cyber-safe CAV system.