Vehicle trajectories from different directions within intersections intersect and create conflicts on a two-dimensional plane. Compared to highways, the lateral movement of vehicles within intersections is irregular and difficult to predict. This risk from lateral trajectories extends to the surrounding areas, impacting other vehicles within the intersection and subsequently reducing both safety and efficiency. To address this issue, a framework for identifying the conflict risk of vehicle trajectory based on lateral movement at intersections is proposed in this paper. Firstly, the concept of virtual lanes is employed to extract abnormal trajectories of lateral vehicle movement within intersections. Subsequently, a spatio-temporal hexahedral conflict detection algorithm, based on the vehicle border, is developed. Finally, the causes of abnormal lateral trajectories within intersections and their relationship with conflict events are discussed in detail, and hotspot areas of vehicle safety risks within intersections are identified. To validate the proposed model, typical intersection vehicle trajectory data captured by uncrewed aerial vehicles is utilized. The research results indicate that abnormal lateral movements of vehicles within intersections are common and pose significant risks to neighboring vehicles. The data reveal a specific pattern in the abnormal trajectory and trajectory density at intersections, with abnormal lateral trajectories of left-turning vehicles accounting for as much as 95.02% of cases. Furthermore, abnormal lateral movement in risky trajectories accounted for up to 86.6%. The proposed method in this paper provides support for intersection safety assessments, abnormal lateral trajectory warning systems, and real-time risk calculations for connected autonomous vehicles and Car2Car communication.

Human-like visual perception systems are indispensable and vital components of human-like autonomous vehicles. In the real driving environment, there is much unlabeled information and the total number of categories of information is uncertain. While human brains are adept at processing such information, current methods are not. Thus, this study presented a novel hybrid unsupervised deep learning method to model the information processing mechanism of the driver’s visual perception. The proposed approach (CAE-SOM) was a neuron-triggered method, which leveraged the virtues of a convolutional autoencoder (CAE) and a self-organizing map (SOM) neural network. The CAE mimicked the hierarchical structures of the driver’s visual system to extract the high-level features, whilst the SOM neural network simulated the working principle of human brain neurons during the information judgment process to perform unsupervised clustering. The CAE-SOM method was built by using a dataset with eight common types of objects in road environments, and then it was tested on a public dataset LabelMe. The results showed that the CAE-SOM method performed well with an average accuracy of 90%. Compared with current unsupervised methods, the CAE-SOM model could improve the accuracy by nearly 10%. Compared with current supervised methods, this new model was still competitive, and its accuracy was close to the highest one. More importantly, the CAE-SOM model could reduce the cost of human labeling work in an unsupervised way and handle data from new categories that had never appeared. The outcomes could contribute to the visual algorithm optimization and safety improvement for autonomous vehicles.

In the context of pro-active traffic management, real-time crash risk evaluation is one of the most critical components. Signalized intersections are well-known high-risk locations because of the variety of traffic movements, modes, and their interactions. Unlike access-controlled freeways, the traffic flow at signalized intersections presents cyclical characteristics, which are temporally separated by traffic signals. Therefore, the data preparation for real-time crash risk prediction at signalized intersections should be based on the signal cycle rather than a predefined fixed time interval (e.g., 5 minutes). In this research, the corresponding signal cycles where crashes occurred were verified based on high-resolution event-based data (i.e., Automated Traffic Signal Performance Measures (ATSPM)). Six types of real-time cycle-level factors were considered, including traffic volume, signal timing, headway and occupancy, traffic variation between upstream and downstream detectors, shockwave characteristics, and weather. Two undersampling strategies, matched case-control and random undersampling, were utilized to develop conditional logistic and binary logistic models, respectively. Model results indicate that the random undersampling performs better than the matched case-control method. It was found that higher cycle volume, overall average flow ratio across lanes, arrivals on yellow ratio, traffic volatility across approach sections, as well as longer cycle length and lower green ratio could significantly increase the odds of crash occurrence at signalized intersections. Moreover, longer maximum queue length, bigger shockwave, and higher absolute queuing shockwave speed tend to increase the odds of crash occurrence.

This research proposes a method to estimate the practical effectiveness of 6 advanced driver assistance systems (ADAS) in avoiding crashes on different roadway facilities (locations). The systems include blind spot warning/lane change warning (BSW/LCW), forward collision warning (FCW), intersection movement assist (IMA), pedestrian crash avoidance and mitigation (PCAM), lane departure warning (LDW) and left turn assist (LTA) systems. The method involves the use of meta-analysis and quasi-induced exposure methodologies to estimate the distribution of effectiveness factors across the driver population. These factors include system operating conditions, system adoption rates and system usage features. Then a Monte-Carlo procedure is applied to sample these factors for simulating the practical ADAS effectiveness. The research shows that the practical effectiveness could be as much as 48.25% lower than the ideal effectiveness proposed by previous research. It is also found that BSW/FCW, PCAM, FCW and LDW systems exhibited the highest effectiveness when operating on urban expressways/freeways. On rural multilane highways, such systems are demonstrated to have the least effectiveness. Furthermore, LTA systems are found to perform better at rural intersections than at urban intersections while the conclusion is contrary regarding IMA systems. Among the six systems, BSW/LCW has the most consistent performance among facilities, while LTA is most likely to be affected by facility type. The integrated system of six ADAS systems could prevent at most 29.50% of total crashes on urban multi-lane highways, while its lowest practical effectiveness is 13.72% on rural two-lane highways. Finally, technical and policy implications of the results are discussed.