Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey.

Merge scenarios on highway are often challenging for autonomous driving, due to its lack of sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow. This, as a result, may impose serious safety risks, and often cause traffic jam with autonomous driving. Therefore, human-like autonomous driving becomes important, yet imperative. This paper presents an interaction-aware decision-making and planning method for human-like autonomous driving in merge scenarios. Rather than directly mimicking human behavior, deep inverse reinforcement learning is employed to learn the human-used reward function for decision-making and planning from naturalistic driving data to enhance interpretability and generalizability. To consider the interaction factor, the reward function for planning is utilized to evaluate the joint trajectories of the autonomous driving vehicle (ADV) and traffic vehicles. In contrast to predicting trajectories of traffic vehicles with the fixed trajectory of ADV given by the upstream prediction model, the trajectories of traffic vehicles are predicted by responding to the ADV's behavior in this paper. Additionally, the decision-making module is employed to reduce the solution space of planning via the selection of a proper gap for merging. Both the decision-making and planning algorithms follow a “sampling, evaluation, and selection” framework. After being verified through experiments, the results indicate that the planned trajectories with the presented method are highly similar to those of human drivers. Moreover, compared to the interaction-unaware planning method, the interaction-aware planning method behaves closer to human drivers.

Traditional modeling methods of steering feedback torque (SFT) widely used in driving simulator are mainly physics-based, but suffers from high model complexity and low model accuracy due to the inaccurate or unknown structure, dynamics, and related parameters of the steering system, such as backlash or dry friction. For these problems, data-driven methods were considered to have advantageous in modeling such a complex yet dynamic system. However, due to the limitation of the data collected for model training from real vehicle test, the commonly used artificial neural network (ANN) cannot fully reflect the steering dynamics, and does not work well in many cases where collected data do not cover, and thus lead to poor generalization performance. In this article, a long short-term memory (LSTM) model is proposed and proved to simulate well on the dynamics of SFT, and works well in most driving conditions. A driving simulator is developed to verify the performance of different models in terms of generalization of working conditions. Based on the driving simulator data, we found that ANN failure generally arises in the transition between conventional and unconventional operating conditions, which is recognized by the proposed driving pattern recognition method. The LSTM-based modeling method can effectively solve this problem. The comparative analysis reveals that the improved LSTM model can simulate the SFT with higher accuracy and broader adaptability to driving simulator conditions.

Modeling driving behavior plays a pivotal role in advancing the development of human-like autonomous driving. In light of this, this paper proposes a car-following behavior modeling method with sample-based deep inverse reinforcement learning (DIRL). Due to the challenges associated with feature extraction and the limited fitting capacity of linear functions, traditional IRL, which employs feature-based linear functions to represent reward functions, exhibits low modeling accuracy. Accordingly, DIRL leverages deep neural networks to represent reward functions. However, the requirement for reinforcement learning to determine the optimal policy for DIRL's reward function makes the training and inference processes computationally resource-intensive and inefficient. To address this issue, this paper proposes the sample-based DIRL. Through solution space discretization, sample-based DIRL streamlines the integration calculation of the partition function into a summation, resulting in improved computational efficiency. Specifically, it is a three-stage framework: sampling candidate trajectories, evaluating candidate trajectories, and selecting the trajectory with the highest reward. In order to evaluate DIRL at both the level of driving behavior and the reward function, the MPC-based virtual driver with the explicit reward function is utilized to collect driving data for training and assessing the convergence of the learned reward function. The experimental results confirm that the proposed method can accurately model the car-following behavior, and acquire the driver's reward function from the driving data.

Human-like autonomous driving can enhance user acceptance and integration within traffic. In light of this, this paper presents a planning method for the human-like longitudinal trajectory in car-following scenarios with offline sample-based maximum entropy deep inverse reinforcement learning (DIRL). Specifically, the proposed method doesn’t mimic human driving behavior directly. Instead, it uses naturalistic driving data to learn the internal reward function that leads to these driving behaviors. To enhance the capacity for fitting the human reward function, DIRL leverages deep neural networks to replace linear functions used by traditional IRL. However, the long-tail effect of naturalistic driving data makes it challenging for DIRL to capture the reward function in edge scenarios. A simulated dataset covering edge scenarios is collected by employing feature-based inverse reinforcement learning to deal with this challenge. Furthermore, this paper trains a long-term actor network guided by DIRL’s reward network. The long-term actor network significantly reduces the computation cost by three orders of magnitude compared to the reward network-based method, while also avoiding system oscillation in contrast to the traditional one-step actor network. The simulation experiments confirm that the planning results from the proposed method are closer to human drivers’ behavior than the baseline. And, the hardware-in-the-loop experiment results affirm the proposed method’s effectiveness and good real-time performance. Note to Practitioners —Human-like autonomous driving can enhance user acceptance and integration within traffic. This paper proposes a planning method for human-like car-following driving behaviors using offline sample-based deep inverse reinforcement learning (DIRL). Instead of mimicking human trajectories directly, the method learns the internal reward function leading to these driving behaviors. To address challenges posed by naturalistic driving data’s long-tail effect, a simulated dataset covering edge scenarios is collected by employing feature-based inverse reinforcement learning. Additionally, this paper trains a long-term actor network guided by DIRL’s reward network to reduce the computation cost. The simulation experiments confirm that the planning results from the proposed method are closer to human drivers’ behavior than the baseline. And, the hardware-in-the-loop experiment results affirm the proposed method’s effectiveness and good real-time performance.

As autonomous driving technology scales up, complex urban intersections pose significant safety challenges. Current testing methods struggle to simulate these complex scenarios at a manageable cost, making simulation testing essential. For effective evaluation, establishing comprehensive and objective complexity metrics is crucial. However, existing complexity evaluation methods often depend on the performance of the primary vehicle and are based on local interaction relationships, which lack a global perspective and objectivity and have yet to be validated by autonomous driving systems. To address this issue, this paper proposes a multidimensional complexity assessment framework that introduces system-level indicators such as vehicle count, interaction density, disorder, and risk. This framework quantifies the complex interactions at intersections from a global perspective, independent of primary vehicle performance. Experimental results demonstrate that the complexity evaluation results are highly consistent with the performance of a high-level autonomous driving system (Apollo). The framework has been successfully applied to test scenario generation on the Apollo platform, achieving twice the scenario generation efficiency of traditional methods, thus showcasing substantial engineering value.