Advances in Vehicle Safety and Crash Avoidance Technologies

1. Introduction

Per the World Health Organization, traffic accidents cause significant financial losses and fatalities annually. Approximately 1.19 million people lose their lives in traffic accidents, and another 20 to 50 million suffer non-fatal injuries, many of whom become permanently disabled as a result. As intelligent transportation technologies continue to evolve, vehicle safety and crash avoidance have emerged as critical topics. In recent years, the research focus has shifted from single-accident analyses to multidimensional risk perception, data-driven decision-making, and human–machine cooperative optimization. Innovative approaches such as autonomous driving, Vehicle-to-Everything (V2X) communications and Advanced Driver Assistance Systems (ADASs) have provided new perspectives for enhancing traffic safety. However, complex road environments, issues of human–machine trust, and the adaptability of existing infrastructure still require in-depth investigations.

Many studies focus on the safety problems brought about by new transportation trends, such as elderly drivers, autonomous vehicles, micro-mobility, and proactive safety analyses. As the population is quickly aging, elderly drivers are becoming increasingly prominent in traffic systems and have become a key research object in safety studies. The research indicates that while elderly drivers exhibit more cautious behaviors in complex environments, their reliance on driver assistance technologies grows significantly [1]. Furthermore, research on autonomous vehicles has emerged as a focal area, with the uncertainty of safety and reliability being quantified [2], which provides critical support for technological advancements in intelligent vehicles. The scope of transportation safety research is expanding to micro-mobility (e.g., electric scooters) devices and two-wheelers, revealing behavioral–infrastructure conflicts in mixed traffic environments [3,4,5]. For instance, in [3], the authors identified safety measures such as separating micro-mobility from vehicles and strengthening enforcement. In [4], a new intelligent prediction model was proposed to enhance the driving safety of two-wheelers through real-time decision-making. Additionally, in [5], the focus is on the collision scenario between automobiles and electric two-wheelers. High-risk scenarios can be generated to test autonomous driving systems and demonstrate that vehicles can handle collision avoidance, which cannot be accomplished with manual driving, thus improving the safety of the driving system and ensuring the driving safety of two-wheelers. In urban contexts, analyses of near-miss incidents demonstrate the predictive power of safety data for real accident prevention and infrastructure optimization [6]. In short, these multidimensional investigations converge on a central challenge: how can technological innovation and systemic optimization work together to enhance proactive safety management in complex human–vehicle–environment interactions?

2. Overview of Published Articles

In terms of vehicle safety and crash avoidance, significant emphasis has been placed on safety research on autonomous vehicles to explore the driving safety of electric scooters and two-wheeled vehicles [3,4,5]. To more accurately predict the occurrence of traffic accidents, multiple methods are adopted to investigate the spatio-temporal dynamics of road crashes, showing that vehicle–pedestrian collisions pose the highest risk, with a significant spatio-temporal clustering of fatal crashes [7]. Notably, simulation experiments demonstrated that both the training efficiency and overall satisfaction of the optimized human–machine interface group were significantly higher than those of the control group [8]. A Controller Area Network bus intrusion detection method based on windowed Hamming distance has been proposed to enhance autonomous driving security, which could achieve an accuracy of 99.67% in detecting Denial of Service attacks while reducing computational time by a factor of 20 compared with traditional deep convolutional neural network, thereby satisfying the real-time requirement of in-vehicle systems [9]. These findings show that improving vehicle safety depends on successfully combining multiple key areas: accurate accident prediction models, better understanding of driver behavior patterns, and strong cybersecurity systems constituting foundational pillars for next-generation intelligent transportation ecosystems. Therefore, in future work, more attention needs to be paid to alternative spatio-temporal clustering methods and their applicability in cities with different urban configurations and socio-economic contexts [7]; effective improvements to the feedback from human–machine interactions with other game elements, task benefits, and psychological factors [8]; and reliable improvements and enhancements to detectable cyber-attack types [9] to improve traffic safety in a more comprehensive manner.

The critical gaps in highway safety research span three domains: geological route optimization, driver behavior, and blind-spot detection systems. In mountainous highway route selection, the Technique for Order Preference by Similarity to an Ideal Solution with weight assigned by the Entropy Weight Method evaluation model was proposed to overcome multi-factor evaluation problems, providing a reliable basis for route scheme decision-making [10]. Eye-tracking experiments showed that drivers in bridge sections focus their visual attention on the left side of the central anti-glare board and that visual attraction environments at highway tunnel entrances significantly impact drivers’ visual attention level, cognitive workload, and subjective mental workload [11,12]. Additionally, research found that decorative longitudinal stripes can enhance spatial perception, alleviate fear, and improve the lateral stability of vehicles, while decorative horizontal stripes have a limited effect on speed regulation [13]. A novel method was also conducted to study the impact of autonomous vehicles equipped with external human–machine interfaces (eHMIs) on pedestrian’s behavior and safety in blind spot scenario. Through Virtual Reality (VR) experiments, eHMI designs based on environmental perception and hazard warnings were found to be more effective [14].

3. Conclusions

The field of highway traffic safety and crash avoidance is undergoing a transformative evolution in vehicular technology and research methodology, with the research interests having shifted from single-crash analyses to data-driven risk perception, human–machine cooperative frameworks, and diverse methodological advances and from spatio-temporal crash modeling to VR-based eHMI studies. Three interlocking challenges now guide future research:

• Real-time, multi-modal data fusion: Multi-modal data such as near-miss, V2X telemetry, driver behavior, and urban infrastructure data need to be integrated into unified, AI-powered risk-assessment engines. Overcoming fragmented data will enable context-aware early warnings and dynamic safety zones.
• Adaptive human–machine collaboration: ADAS and autonomous decision-making need to be grounded in cognitive–behavioral models of attention and workload, and transparent, explainable interfaces (both in-vehicle and via eHMI) that adapt to driver state and environmental complexity need to be developed, ensuring flexible cooperative control and safety driving.
• Policy adaptability: A stratified approach urgently needs to be taken in creating regulatory standards for emerging transportation modes (e.g., electric scooters) and mixed traffic environments.

From risk perception among micro-mobility users to route optimization in complex highway environments and from in-vehicle network security to the fine-grained monitoring of driver behavior, these studies collectively develop a multi-layered blueprint to enhance road safety. They provide scientific evidence for traffic management agencies and pave the way forward for future developments of autonomous driving and vehicle–infrastructure cooperative technologies. In summary, vehicle safety research is transitioning from isolated technological breakthroughs to a systematic reconfiguration of “data, behavior, and environment”, with the development of technology, policy, and user education emerging as the key pathway toward the vision of zero fatalities.