Predicting vulnerableroad user behavior is an essential prerequisite for deploying Automated Driving Systems (ADS) in the real-world. Pedestrian crossing intention should be recognized in real-time, especially for urban driving. Recent works have shown the potential of using vision-based deep neural network models for this task. However, these models are not robust and certain issues still need to be resolved. First, the global spatio-temporal context that accounts for the interaction between the target pedestrian and the scene has not been properly utilized. Second, the optimal strategy for fusing different sensor data has not been thoroughly investigated. This work addresses the above limitations by introducing a novel neural network architecture to fuse inherently different spatio-temporal features for pedestrian crossing intention prediction. We fuse different phenomena such as sequences of RGB imagery, semantic segmentation masks, and ego-vehicle speed in an optimal way using attention mechanisms and a stack of recurrent neural networks. The optimal architecture was obtained through exhaustive ablation and comparison studies. Extensive comparative experiments on the JAAD and PIE pedestrian action prediction benchmarks demonstrate the effectiveness of the proposed method, where state-of-the-art performance was achieved. Our code is open-source and publicly available: https://github.com/OSU-Haolin/Pedestrian_Crossing_Intention_Prediction .

Lane change maneuver of high-speed automated vehicles is complicated since it involves highly nonlinear vehicle dynamics, which is critical for the driving safety and handling stability. Addressing this challenge, we present the dynamic modeling and control of high-speed automated vehicles for lane change maneuver. A nonlinear single-track vehicle dynamics model and a multi-segment lane change process model are employed. Variable time-steps are utilized for the vehicle model discretization to ensure a long enough prediction horizon while maintaining model fidelity and computational feasibility. Accordingly, the control of lane change maneuver is addressed in two successive stages. First, by considering the lane change maneuver as primarily a longitudinal control problem, velocity profiles are determined to ensure the longitudinal safety of this maneuver. Then, the associated lateral control is generated with a model predictive controller, taking the handling stability envelope, coupled tire forces and environmental constraints into account. Simulations demonstrate the real-time ability and stable-handling capability of the proposed approach.

Social distancing (SD) is an effective measure to prevent the spread of the infectious Coronavirus Disease 2019 (COVID-19). However, a lack of spatial awareness may cause unintentional violations of this new measure. Against this backdrop, we propose an active surveillance system to slow the spread of COVID-19 by warning individuals in a region-of-interest. Our contribution is twofold. First, we introduce a vision-based real-time system that can detect SD violations and send non-intrusive audio-visual cues using state-of-the-art deep-learning models. Second, we define a novel critical social density value and show that the chance of SD violation occurrence can be held near zero if the pedestrian density is kept under this value. The proposed system is also ethically fair: it does not record data nor target individuals, and no human supervisor is present during the operation. The proposed system was evaluated across real-world datasets.

This paper investigates the cooperative and flexible vehicle platooning problem in automated highway systems. We formulate the platoon into a dynamically decoupled system and address the flexible platooning problem using distributed model predictive control (DMPC) techniques. A two-step noniterative DMPC strategy is proposed that sequentially solves local components of a constrained optimal control problem over two batches of vehicle clusters based on intervehicle communication. By introducing the clustering procedure, the proposed DMPC scheme only requires compatibility constraints of common neighbors of two adjacent vehicle clusters. With the two-step DMPC scheme, stability of the overall closed-loop system is guaranteed by a series of linear matrix inequalities that can be efficiently solved in practical applications. Collision-free properties are addressed using coupled state constraints together with terminal sets during a cooperation procedure. Simulations of flexible platooning are conducted for both joining and leaving events to show the effectiveness of the two-step DMPC scheme.

How many scenarios are sufficient to validate the safe Operational Design Domain (ODD) of an Automated Driving System (ADS) equipped vehicle Is a more significant number of sampled scenarios guaranteeing a more accurate safety assessment of the ADS Despite the various empirical success of ADS safety evaluation with scenario sampling in practice, some of the fundamental properties are largely unknown. This paper seeks to remedy this gap by formulating and tackling the scenario sampling safety assurance problem from a set invariance perspective. First, a novel conceptual equivalence is drawn between the scenario sampling safety assurance problem and the data-driven robustly controlled forward invariant set validation and quantification problem. This paper then provides a series of complete solutions with finite-sampling analyses for the safety validation problem that authenticates a given ODD. On the other hand, the quantification problem escalates the validation challenge and starts looking for a safe sub-domain of a particular property. This inspires various algorithms that are provably probabilistic incomplete, probabilistic complete but sub-optimal, and asymptotically optimal. Finally, the proposed asymptotically optimal scenario sampling safety quantification algorithm is also empirically demonstrated through simulation experiments.

Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments.