Autopilot "jumping" is not that simple

Autopilot "jumping" is not that simple When it comes to deploying higher-level autonomous driving systems, the first step is choosing the right environment. Compared to complex urban traffic, highways may be a more reliable and manageable destination. In such scenarios, current laser sensors face challenges, making long-range camera vision essential. Additionally, the high cost and limited capacity of LiDAR also pose barriers to the commercialization of fully autonomous vehicles. Let's take a closer look at what car electronics experts have to say.  Autopilot "jumping" is not that simple Going from Level 2 to Level 4 autonomy isn’t as straightforward as just moving from one corner to another. According to current autonomous driving technology, the jump from L2 to L3 is a qualitative shift. However, companies like Volvo, Ford, and Toyota are now aiming to skip L3 and directly move toward full L4 autonomy. The U.S. National Highway Traffic Safety Administration (NHTSA) categorizes autonomous vehicles into five levels: Driver Assistance (L1), Partial Automation (L2), Conditional Automation (L3), High Automation (L4), and Full Automation (L5). At Level 4, the vehicle can operate without human intervention under certain conditions, while Level 5 represents full autonomy with no need for a driver at all. “Level 3 is a transition between human and machine control,” explains Chen Chaozhuo, a research director at the China-Sweden Traffic Safety Center. “Drivers must stay alert and ready to take over at any moment, which creates a contradiction. They’re supposed to be hands-off but still responsible.” This contradiction has led some automakers to bypass L3 altogether. Volvo, for example, has already developed an L4 autonomous vehicle and plans to test it globally before mass production in 2021. Similarly, Ford has chosen to focus on L5 technology instead of L3, believing that involving the driver adds unnecessary complexity and risk. Chen points out that L3 is not only legally problematic but also dangerous. “If a system fails and the driver isn’t ready to take over, it could lead to serious safety issues.” That’s why many companies prefer to go straight to L4 or even L5. But achieving L4 autonomy isn’t easy either. “It’s a technical nightmare,” says Chen. “At L4, the system must perceive its environment almost perfectly—close to 100% accuracy. Current sensors, whether cameras, LiDAR, or radar, still struggle with real-world unpredictability.” For example, in areas like Beijing’s Fifth and Sixth Ring Roads, where pedestrians and cyclists behave unpredictably, even advanced systems find it hard to interpret intentions. “Can an L4 car really tell if an elderly person wants to cross the road or just stand there?” asks Chen. Moreover, cybersecurity remains a major concern. If an autonomous vehicle is hacked, the consequences could be life-threatening. As Tian Daxin from Beihang University notes, “Unmanned vehicles will become agents in the future. If they fall into the wrong hands, it’s not just about property loss—it’s about human lives.” Despite these challenges, some experts believe that L4 autonomy could become feasible by 2030. However, others, like Pony.ai founder Peng Jun, think it might take longer than that to achieve full coverage in cities like Beijing. To accelerate progress, Baidu launched the Apollo project, offering an open platform for automakers and developers to build their own autonomous systems. By integrating cameras and other sensors, Baidu aims to make L3-level autonomy more accessible and affordable. Yet, success won’t come easily. The Apollo program requires not only technical leadership in AI and algorithms but also widespread industry support. Still, the consensus is clear: autonomous driving needs to be deployed in the right environments first. Hao Jianan from Tucson believes that highways, with their predictable conditions, are a better starting point than complex urban settings. “High-speed scenarios require long-range sensing, which is challenging for current LiDAR and cameras. Cost and performance are key factors in choosing the right technology.” In conclusion, functional safety and redundancy are critical when transitioning from human-driven to machine-driven vehicles. Every component must be reliable, and backup systems must be in place to ensure passenger safety. As the industry continues to evolve, the path to full autonomy remains complex—but it’s a journey worth taking. Stay tuned for more updates on the future of autonomous driving.

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