Following successful achievements in quantum technology applied to automotive batteries, software-defined vehicle (SDV) design, and optimization of vehicle connectivity systems, car manufacturers, particularly Tesla, are increasingly directing their applications toward advanced driver assistance systems (ADASs). They anticipate leveraging the benefits of quantum AI and integrating it into Tesla’s future Full Self-Driving (FSD) capability.
ADASs: A quick overview
ADASs are electronic systems that enhance driver safety and improve the driving experience. These systems use various technologies such as sensors, cameras, radar, and lidar to provide real-time information and driver assistance. They have features such as adaptive cruise control (ACC) for proactive distance adjustment between vehicles, lane departure warning, and lane-keeping assistance. They also have functionalities designed to protect other vehicles or pedestrians, such as forward collision warning, blind-spot detection, pedestrian detection, and parking assistance.
Implementing all these features relies on two key components: sensors and AI. Vehicles with ADAS technologies require many sensors to provide comprehensive and complete data on various aspects, such as the vehicles’ interiors and their surrounding environments. Subsequently, smart decision-making is facilitated through on-board AI or cloud-based AI, enabling autonomous or unmanned driving functionality.
How quantum computing can enhance ADASs
Compared to traditional vehicle sensors, quantum sensors, which utilize properties of quantum mechanics such as quantum entanglement and quantum superposition, open up more possibilities for autonomous vehicles. While these state-of-the-art sensors are still in their early development, they have the potential to make ADASs more robust and reliable with these features:
- Enhanced perception capability. Integrating quantum sensors into ADASs can enhance a vehicle’s perception capability. The high sensitivity and precision of quantum sensors can detect subtle changes in the surrounding environment, as when identifying obstacles on the road and the position and movement of other vehicles or pedestrians, thus enabling the systems to be more alert to potential risks at an earlier stage.
- Accurate positioning and navigation. Leveraging the high-precision measurement capability of quantum sensors can achieve more accurate positioning and navigation functions. These can improve vehicle positioning, road map updates, and automatic driving path planning of ADASs, enabling vehicles to drive more accurately and cope better with complex traffic situations.
- Reduced false alarms and misjudgments. Quantum sensors’ high sensitivity and low noise characteristics can help reduce false alarms and misjudgments in ADASs. By using precise data provided by quantum sensors, the systems can better distinguish genuinely dangerous situations from other, harmless environmental changes, enhancing the reliability and performance of the systems.
- Enhanced self-learning and optimization. Combining quantum sensors with the intelligent learning and optimization functions of ADASs can achieve a higher level of self-learning and optimization. The systems can improve their behavior and decision-making based on the data obtained from quantum sensors, thereby continuously improving their performance and adaptability.
ADASs require significant computing power for AI functions, such as that offered by NVIDIA’s Orin X computing chip, which can provide 254 trillion operations per second (TOPS) of computing power per chip. The Tesla Model 3 Highland version offers even higher computing power, reaching 720 TOPS. In addition to CPU and GPU enhancements to boost performance and provide more computing power, NPUs (neural processing units) have recently begun to thrive. Moreover, QPUs (quantum processing units) are considered the future stars, promising higher computational power for in-car use.
On top of providing high computational power within vehicles, quantum computers can offer even greater, better, and more stable computational power in the cloud. It is not surprising that IBM launched its first quantum data center in Europe last year.
Safety and security risks
As quantum computing technology becomes widely applied in autonomous vehicles alongside its twin, AI, concerns arise regarding the disruption of quantum calculations or the interference with quantum computer operations — potentially leading to safety or security issues, not to mention risks to human life.
The key risk is quantum decoherence: The core, which provides high computing power in quantum computers, can be disrupted. Here are some of the ways that it can be disrupted:
- Decoherence due to environmental interactions. Interactions with the environment, such as thermal fluctuations, electromagnetic fields, or other forms of noise, can lead to decoherence. This typically occurs when a quantum system becomes entangled with its surroundings, causing loss of coherence.
- Scattering and collision processes. Interactions with other particles, such as scattering events or collisions, can cause phase shifts or alterations in the quantum state, leading to decoherence.
- Imperfect control and measurement. Inaccuracies in controlling or measuring quantum systems can introduce errors and disrupt coherence. These include errors in gate operations, imperfect measurements, or limitations in experimental apparatus.
- Decoherence by dissipation. Energy dissipation processes, such as coupling to a thermal reservoir, can lead to decoherence. This results in loss of quantum information to the environment, making it difficult to maintain coherence over time.
- Environmental fluctuations. Fluctuations in the properties of the environment, such as temperature, pressure, or electromagnetic fields, can perturb a quantum system and cause decoherence.
- Quantum interference from external sources. External sources of interference, such as stray electromagnetic fields or background radiation, can interfere with the coherent evolution of a quantum system.
- Geometric phases and topological effects. While geometric phases and topological effects can lead to robustness against certain types of decoherence mechanisms, specific geometric phases can also be susceptible to some perturbations.
- Loss of entanglement. Entanglement with other systems can protect a quantum system against decoherence. However, loss or degradation of entanglement due to interactions with the environment can lead to the breakdown of coherence in the quantum system.
Understanding and mitigating these disruptions are critical for the development and practical implementation of quantum technologies, especially in ADASs.
Conclusion
The immense computational power and the precise environmental monitoring afforded by quantum technology provide the necessary resources for ADAS operations. Consequently, the integration of quantum technology into ADAS applications is expected not only to become mainstream but also to bring significant economic impact on the automotive industry. According to a McKinsey report, this disruptive technology is poised to rake in between US$2 billion and US$3 billion for the automotive industry by 2030.
While the automotive industry eagerly embraces quantum technology, it is imperative to remain vigilant of the risks associated with quantum technology and implement mitigation strategies to ensure the security and safety of vehicles.
