The mobility of the future will be largely shaped by autonomous vehicles. However, not all developments are going according to plan: General Motors recently announced that it and its subsidiary Cruise would be withdrawing from the development of robotaxis. This move has shaken up the industry and raises the question of how stable progress in autonomous driving really is. While some players such as Tesla and Waymo continue to pursue ambitious plans, others are facing technological, regulatory and economic challenges.
Autonomous vehicles are based on the interaction of different technologies. LiDAR, radar and camera systems are becoming increasingly precise and cost-effective. Advances in deep learning and neural networks enable more precise environment recognition and decision-making. At the same time, 5G technologies ensure fast and stable communication between vehicles (V2V) and between vehicles and infrastructure (V2I). The major players in the field of autonomous driving are pursuing different technological approaches that reflect their respective strategies and goals. Tesla is taking an unorthodox approach by relying exclusively on camera-based systems and foregoing LiDAR. The company is convinced that advanced neural networks and high-resolution cameras can provide the necessary environment recognition. Waymo, on the other hand, uses a combination of cameras, LiDAR and radar to enable a comprehensive and redundant perception of the surroundings. Another unique selling point of Waymo is the detailed mapping of the areas of operation. The vehicles navigate on high-precision, pre-coded digital maps created specifically for urban areas. This approach offers a high level of security, but requires considerable resources to create and maintain the maps, resulting in a limited number of locations and cities. Volkswagen is pursuing a hybrid strategy and is integrating both LiDAR and camera-based systems into its autonomous vehicles. In collaboration with partners such as Argo AI, the Group is focusing on deployment in urban traffic scenarios as well as long-term plans for autonomous delivery services. Although General Motors (GM) recently announced a withdrawal from certain areas of autonomous driving, Cruise has so far relied on extensive test fleets with advanced sensor stacks. These include LiDAR, radar and cameras specifically designed for safety and reliability. Zoox, a subsidiary of Amazon, is developing specialised autonomous vehicles without steering wheels or pedals that are designed from the ground up as robotaxis. Zoox uses a combination of sensor technology and AI to enable bidirectional movement and maximum flexibility in urban areas. Baidu, on the other hand, is pursuing an open approach with its Apollo platform, which offers developers worldwide access to modular technologies. These can be adapted to specific use cases, from autonomous cabs to logistics solutions. The different approaches reflect not only technological priorities, but also economic and strategic considerations. The choice of technologies has a direct impact on the cost, scalability and marketability of autonomous systems.
Tesla holds a special position in the field of autonomous driving. The company relies on a camera-based system and deliberately avoids the use of LiDAR, which sets it apart from many of its competitors. Tesla's approach is based on the assumption that perception by cameras in combination with advanced software is sufficient to precisely capture a vehicle's surroundings. This strategy offers clear advantages in terms of cost and scalability, as LiDAR systems are currently still expensive and resource-intensive. Another key competitive advantage of Tesla is the huge database that the company generates from the millions of vehicles owned by customers. Every Tesla vehicle is equipped with sensors that continuously collect data and forward it to the company's central AI infrastructure. This "fleet learning" strategy allows Tesla to improve its algorithms faster and more efficiently than competitors, who often rely on smaller test fleets. In addition, Tesla differentiates itself by integrating its technologies directly into an existing and widely used vehicle portfolio. While companies like Waymo and Zoox are developing specialised vehicles for autonomous applications, Tesla is using its existing models to gradually introduce autonomous features. This strategy allows the company to increase consumer acceptance while generating revenue through vehicle sales. The Full Self-Driving (FSD) software is another element that sets Tesla apart from its competitors. Although the FSD software has not yet reached full autonomy, it already offers a wide range of driver assistance functions and is continuously improved through over-the-air updates. This strengthens Tesla's position as an innovative market player, as customers can always use the latest technology without having to buy a new vehicle.
Despite the impressive progress, there are still challenges, but these also offer immense opportunities and possibilities. Autonomous vehicles can make road traffic safer by minimising human error, which is currently responsible for most accidents. Automation could also lead to a more efficient use of resources by making better use of vehicles and optimising routes. In logistics and freight transport, autonomous systems can revolutionise supply chains, reduce costs and ensure a more reliable supply. Autonomous driving services could also improve the quality of life in cities, as fewer vehicles would be needed and traffic jams would be reduced. Mobility could be made more accessible, which would benefit older people and people with disabilities. Autonomous technologies also provide the basis for innovative business models. Ride-sharing services could be operated more cost-effectively and efficiently, while car manufacturers themselves could tap into new sources of income through subscription-based software updates. The digitalisation of mobility has the potential to create more sustainable transport solutions by combining electric vehicles with autonomous systems. Regulating and standardising the technology remains a key challenge, but also offers the opportunity to set global benchmarks for safety and efficiency. With a clear goal and close collaboration between governments, industry and research, autonomous driving could lay the foundations for a new era of mobility.
Autonomous driving is at a crucial point. While the technological breakthroughs are impressive, implementation in everyday life involves technical as well as social and political hurdles. Cooperation between industry, politics and research is essential in order to set global standards and create trust in the new technology. Tesla has a clear advantage with its data-driven strategy and integration into existing vehicle models, while Waymo and others are relying on technologies that require high levels of investment. The conclusion is clear: autonomous driving has the potential to fundamentally change mobility.
