I. Challenges of Intelligent Driving
1. Technological Challenges
Sensor performance limitations: Sensors such as lidar, cameras, and millimeter-wave radar have limited perception capabilities in adverse weather conditions, making it difficult to accurately identify obstacles and road conditions.
Complex decision-making algorithms: Autonomous driving systems need to make safe, efficient and compliant decisions in complex traffic environments, which places extremely high demands on the intelligence and adaptability of the algorithms.
Cross-domain technology integration is difficult: autonomous driving technology integrates multiple fields such as computer vision, sensor technology, communication technology, and artificial intelligence algorithms, making integration very difficult.
High computing power requirements: Processing large amounts of sensor data and complex algorithms requires powerful computing capabilities, increasing vehicle costs and energy consumption.
Insufficient data richness: Autonomous driving technology requires a large amount of rich scene data for algorithm training, but the cost of acquiring edge scene data is high and a large amount of driving mileage needs to be accumulated.
2. Regulatory Challenges
Inadequate laws and regulations: The development of autonomous vehicles requires corresponding laws and policies, but the current laws and regulations are not yet perfect, which restricts commercial application.
Inconsistent international regulations: Different countries have different laws and regulations on autonomous driving, which increases the difficulty of cross-border research and development and operation.
3. Infrastructure challenges:
Inadequate road infrastructure: Autonomous vehicles need to interact with road infrastructure, but much of the infrastructure is not yet fully adapted to the needs of autonomous vehicles, affecting performance and safety.
Lagging communication infrastructure: Delays in communication between vehicles and between vehicles and infrastructure can affect real-time decision-making and control.
4. Social Acceptance Challenges
Lack of public trust: Due to reports of autonomous driving accidents, the public has doubts about the safety and reliability of the technology, affecting its widespread application and promotion.
Ethical and moral controversies: Autonomous driving systems may involve ethical issues when handling emergencies, such as choosing which object to protect in a collision, where there is a lack of clear ethical guidelines.
5. Economic challenges
High R&D costs: From sensor development to algorithm optimization, and then to large-scale testing and verification, huge financial investments are required.
II. Safety Precautions for Intelligent Driving
According to the national recommended standard "Classification of Driving Automation for Automobiles" approved and issued by the State Administration for Market Regulation (Standardization Administration), autonomous driving technology is divided into six levels from L0 to L5. Among them, L1 to L2 level autonomous driving is called "intelligent assisted driving". The system assists the human to perform autonomous driving tasks in some environments, and the human needs to be ready to take over at any time. Even in the L3 to L4 stage, the human still needs to maintain the ability to take over and deal with special situations in a timely manner. Only in the L5 stage, the system achieves a high degree of automation and no longer requires human intervention.
Currently, all commercially available automated driving assistance systems in China are Level 2. This means that even after activating the intelligent assistance system, problems such as scene recognition failure, misidentification, and target loss may still occur. For example, if a static object such as an overturned truck appears on a highway, the driving assistance system is likely to malfunction, and if the driver does not take decisive action, the consequences could be disastrous.
For some time now, traffic accidents caused by the malfunction of intelligent driver assistance systems have occurred frequently, some with extremely serious consequences, providing profound lessons. Some drivers mistakenly believe that "assisted driving" means the car will drive itself, and that they can completely disregard the driver's responsibilities, resulting in disastrous consequences. Numerous cases show that most drivers are not truly confident in autonomous driving, but rather their weak awareness of traffic safety and the law, coupled with a growing sense of complacency that breaches their mental defenses for safe driving. For these individuals, even without intelligent driver assistance systems, they might still use their phones or doze off while driving, leaving safety hazards ever-present. Repeatedly crossing legal boundaries makes an accident inevitable.
As a relevant official from the Traffic Management Bureau of the Ministry of Public Security stated, "Every person and every vehicle involved in road traffic can have a significant impact on road traffic safety. Human factors account for more than 90% of the causes of road traffic accidents." Car accidents are more dangerous than tigers. Past traffic accidents have repeatedly confirmed the principle that "luck is the origin of misfortune, and misfortune will inevitably become the end of luck," and serve as a warning to people: While autonomous driving may be easy and comfortable, safe driving cannot tolerate the slightest carelessness. A little laziness or shortcuts can easily lead to a lifetime of regret.
With continuous breakthroughs in intelligent driving technology, my country is accelerating its application. While we are pleased with this progress, we must also be aware that we must keep pace with the times and improve our road traffic safety governance capabilities and strengthen the investigation and rectification of potential hazards. This involves multiple stakeholders, including government departments, car manufacturers, internet platforms, and drivers, and requires the joint efforts and coordinated advancement of all parties.
For public security and traffic management departments, the first step is to put in the hard work of analyzing the patterns and characteristics of driving violations committed using intelligent assisted driving modes, and to proactively fulfill their regulatory responsibilities by addressing problems early and strictly enforcing regulations. Secondly, they must leverage technology effectively, accelerate the implementation of a big data strategy, create a new "smart traffic management" model, improve the rate of violation detection and punishment, and enhance deterrence.
