End-to-end approaches are one of the most promising solutions for autonomous vehicles (AVs) decision-making. However, the deployment of these technologies is usually constrained by the high computational burden. To alleviate this problem, we proposed a lightweight transformer-based end-to-end model with risk awareness ability for AV decision-making. Specifically, a lightweight network with depth-wise separable convolution and transformer modules was firstly proposed for image semantic extraction from time sequences of trajectory data. Then, we assessed driving risk by a probabilistic model with position uncertainty. This model was integrated into deep reinforcement learning (DRL) to find strategies with minimum expected risk. Finally, the proposed method was evaluated in three lane change scenarios to validate its superiority.

Distracted driving has been recognized as a major challenge to traffic safety improvement. This article presents a novel driving distraction detection method that is based on a new deep network. Unlike traditional methods, the proposed method uses both temporal information and spatial information of electroencephalography (EEG) signals as model inputs. Convolutional techniques and gated recurrent units were adopted to map the relationship between drivers’ distraction status and EEG signals in the time domain. A driving simulation experiment was conducted to examine the effectiveness of the proposed method. Twenty-four healthy volunteers participated and three types of secondary tasks (i.e., cellphone operation task, clock task, and 2-back task) were used to induce distraction during driving. Drivers’ EEG responses were measured using a 32-channel electrode cap, and the EEG signals were preprocessed to remove artifacts and then split into short EEG sequences. The proposed deep-network-based distraction detection method was trained and tested on the collected EEG data. To evaluate its effectiveness, it was also compared with the networks using temporal or spatial information alone. The results showed that our proposed distraction detection method achieved an overall binary (distraction versus nondistraction) classification accuracy of 0.92. In terms of task-specific distraction detection, its accuracy was 0.88. Further analysis on the individual difference in detection performance showed that drivers’ EEG performance differed across individuals, which suggests that adaptive learning for each individual driver would be needed when developing in-vehicle distraction detection applications. Note to Practitioners—Driver distraction detection is crucial for safety enhancement to avoid crashes caused by nondriving-related activities, such as calling and texting while driving. Related previous studies mainly focus on detection by monitoring head and eye movement using computer vision technologies or by extracting indicators from driving performance measures for driver state inference. However, complex traffic environments (e.g., dynamically changing light distribution on driver’s face and nighttime driving with low illumination) strongly limit the effectiveness of computer vision technologies, and the driving performance characteristics may also be caused by factors other than distraction (e.g., fatigue). To solve these problems, this article seeks to develop a deep learning-based approach to map the unique relationship between driver distraction and the bioelectric electroencephalography (EEG) signals that are not affected by traffic environments. The proposed method can be integrated into the driver assistance systems and autonomous vehicles to deal with emergency situations that need drivers to handle. The timely detection of distraction by our method will significantly facilitate its practical applications in collision avoidance or danger mitigation in the handover process.

The new energy vehicle plays a crucial role in green transportation, and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving. This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems. Additionally, it envisions the outlook for autonomous intelligent hybrid electric vehicles, with reinforcement learning as the foundational technology. First of all, to provide a macro view of historical development, the brief history of deep learning, reinforcement learning, and deep reinforcement learning is presented in the form of a timeline. Then, the comprehensive survey and review are conducted by collecting papers from mainstream academic databases. Enumerating most of the contributions based on three main directions—algorithm innovation, powertrain innovation, and environment innovation—provides an objective review of the research status. Finally, to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles, future research plans positioned as “Alpha HEV” are envisioned, integrating Autopilot and energy-saving control.

Birds-Eye-View (BEV) perception can naturally represent natural scenes, which is conducive to multimodal data processing and fusion. BEV data contain rich semantics and integrate the information of driving scenes, which play an important role in researches related to autonomous driving. However, BEV constructed by single vehicle perception encounter certain issues, such as low accuracy and insufficient range, and thus cannot be well applied to scenario understanding and driving situation prediction. To address the challenges, this paper proposes a novel data-driven approach based on vehicle-to-everything (V2X) communication. The roadside unit or cloud center collects local BEV data from all connected and automated vehicles (CAVs) within the control area, then fuses and predicts the future global BEV occupancy grid map. It provides powerful support for driving safety warning, cooperative driving planning, cooperative traffic control and other applications. More precisely, we develop an attention-based cooperative BEV fusion and prediction model called BEV-V2X. We also compare the performance of BEV-V2X with that of single vehicle prediction. Experimental results demonstrate that our proposed method achieves higher accuracy. Even in cases where not all vehicles are CAVs, the model can still comprehensively estimate and predict global spatiotemporal changes. We also discuss the impact of the CAV rate, single vehicle perception ability, and grid size on the fusion and prediction results.