Autonomous vehicle sensors in self‑driving cars: an in-depth overview

October 29, 2026
Aleksandr Ahramovich

by Aleksandr Ahramovich , 

Head of AI/ML Center of Excellence

Autonomous vehicle sensors - primarily lidars, radars, and cameras - provide the real-time situational awareness necessary for safe, autonomous navigation. By continuously scanning surroundings, these sensors enable AI software to plan paths and maneuver without human input, reducing accident rates.

Despite their essential role, sensors are only one component of a typical autonomous driving system architecture, alongside automotive software powered by AI for sensor data processing and subsequent decision-making.

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Autonomous vehicle sensors in self‑driving cars

Types of sensors for autonomous driving

Autonomous cars can be powered by different types of solutions, such as camera sensors, lidar systems, and GPS, that facilitate object detection, distance measurement, and vehicle localization.

Conventional cameras capture visible light to produce high-resolution 2D images or video streams. Artificial intelligence systems then process this visual data with computer vision algorithms to identify objects like vehicles or pedestrians, detect lane markings, read traffic signs, and recognize the color of traffic lights. Self-driving vehicles often rely on stereo cameras, which use two spatially separated sensors to estimate object distance based on image disparity for superior spatial awareness. Infrared cameras are also commonly used in autonomous vehicles (AVs) as they’re less susceptible to poor weather or lighting conditions than traditional cameras and can detect pedestrians or animals even in complete darkness.

Strengths

Camera systems are affordable and particularly effective for object detection and recognition, including traffic lights and road signs, delivering rich visual detail.

Limitations

Not effective for depth estimation

Light Detection and Ranging systems continuously emit pulsed laser beams and measure the time for the light to return after reflecting off surrounding vehicles, pedestrians, or other obstacles to estimate their distance. They can also measure the speed of these objects based on the change in frequency of the reflected light (Doppler effect). To ensure a 360-degree field of view, these devices can be mounted on a rotating pod or use multiple emitters. Based on the collected spatial data, lidar systems generate highly accurate 3D maps of the environment in real-time, usually called point clouds. AI-powered software can then analyze these detailed 3D models to recognize object shapes and classify the detected objects. This makes lidar technology a core component of Advanced Driver Assistance Systems (ADAS).

Strengths

Lidar offers unmatched 3D mapping accuracy and performs reliably in complete darkness, making it superior to standard cameras for night-time obstacle detection.

Limitations

Typically expensive, and performance can be affected by heavy rain, dust, and smoke, as these can scatter the light

Radio detection and ranging systems use a similar approach to lidar technology, but rely on the bouncing of radio waves rather than laser light to detect objects in the vehicle’s surroundings and measure the distance and speed of these objects. Furthermore, radar systems typically generate 2D images rather than 3D maps. Due to their relatively long range, they’re very useful for autonomous driving features like adaptive cruise control and automated emergency braking.

Strengths

Radar sensors are inexpensive, provide dependable long-range detection, and are resilient to adverse weather conditions.

Limitations

Relatively low object detection accuracy, making it difficult to distinguish small or closely spaced objects

Ultrasonic sensors serve as a short-range counterpart to radar, using high-frequency sound waves instead of radio waves to detect nearby objects with excellent accuracy. This capability, combined with their affordable price, makes them ideal for parking assistance and a key feature in newer car models.

Strengths

Ultrasonic sensors are extremely accurate at close range, rather reliable in adverse weather conditions, and usually the cheapest sensor type.

Limitations

Poor detection range

The Global Positioning System is the most widely used global navigation satellite system (GNSS). In self-driving vehicles, GPS devices can determine the vehicle’s exact coordinates and speed, which are essential for path planning and navigation.

Strengths

GPS enables global, real-time vehicle positioning requiring no local infrastructure.

Limitations

Vehicle position accuracy affected by tunnels and dense urban areas or “urban canyons” due to limited satellite visibility

Inertial measurement units (IMUs) leverage gyroscopes and accelerometers to measure the vehicle’s speed, direction of motion, and acceleration. Self-driving vehicles can use these devices for a navigation method called “dead reckoning”, calculating the vehicle’s current position based on previously determined location data (typically via GPS) and IMUs’ estimates. This proves essential in GPS-denied environments, such as when the vehicle enters a tunnel or parking garage, enabling relatively accurate positioning and navigation even when satellite signal is lost.

Strengths

IMUs are affordable and crucial for dealing with GPS signal loss.

Limitations

Prone to drift (an accumulation of small errors in measurement) over time, which progressively reduces the accuracy of the estimated position

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Real-world examples of autonomous vehicle sensor adoption

Leading companies, such as Waymo, Mercedes‑Benz, and Tesla, equip cars with sensors to drive vehicle autonomy at different levels.

Launched in 2020, Waymo's robotaxi ride-hailing service was the first to operate without a backup human driver on board. Waymo’s Level 4 fully autonomous driving system, called Waymo Driver, uses machine learning-powered software to process real-time data from radar, lidar, and camera sensors, combined with prebuilt custom maps of the service area. In fact, despite their high level of autonomy, Waymo's robotaxis serve customers only in controlled, geofenced environments, which have progressively expanded over the years.

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Mercedes’ luxury saloon car ranges feature a proprietary Level 3 automated driving system named DRIVE PILOT, which allows drivers to take their hands off the steering wheel and even watch a movie, but requires them to take control when requested. The system relies on multiple sensors, including cameras, radar, lidar, and ultrasonic sensors, to monitor the driving surroundings and make informed decisions autonomously.

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While its name suggests complete autonomy, Tesla's Full Self-Driving technology is currently classified as a Level 2 automated driving system, meaning the driver should constantly monitor the road and be ready to take control immediately. That said, the system can still perform most driving maneuvers with high accuracy, including route navigation, steering, and lane changes.

One of the company’s most noteworthy technological advancements is Tesla’s “Vision”, a system that relies exclusively on high-resolution cameras and AI processing. Instead of leveraging expensive lidar sensors, Tesla uses systems based on neural networks to turn 2D images into 3D models for spatial awareness, significantly lowering production costs.

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Overcoming autonomous vehicle sensor implementation challenges

Businesses need to know how to handle technical and financial obstacles associated with implementing self-driving car solutions to ensure their reliable performance and cost-efficiency.

Challenge

Solution
Adverse environmental conditions
Operating reliably in adverse weather remains a problem for self-driving technology. Rain, fog, and snow can scatter sensor signals and reduce visibility.

To mitigate the effects of weather-related signal distortion, engineers use sensor fusion, building autonomous driving systems that process data from weather-resilient radars, high-precision lidars, and other devices within automotive IoT systems to maintain safety.

Additionally, at the AI model level, companies can opt for data augmentation for model training. By generating modified versions of existing data (different illumination, rotations, etc.), software engineers create larger and more diverse datasets for AI model training, making it more effective at handling suboptimal real-world conditions.

Blind spots
Physical obstacles, such as buildings, large trucks, or even tunnels, can block the line of sight of onboard sensors, limiting driverless vehicles’ situational awareness.

For specific tasks, such as positioning, IMU-enabled dead reckoning can help automated vehicles navigate within enclosed spaces.

In most other cases, Vehicle-to-Everything or V2X communication systems can be a good solution to address blind spots.

This technology allows cars to “talk” to traffic lights and other vehicles. By sharing real-time data, cars can see around corners or through buildings, effectively eliminating blind spots and improving traffic flow in smart cities.

Implementation costs
Despite sensors’ decreasing cost, some of the most effective ones, such as lidars, are still quite expensive.

Prioritize the sensors that best match the vehicle’s operating conditions, usually referred to as the “operational design domain”. For instance, long-range forward-facing radars can be particularly useful for a highway pilot system, while 360-degree high-resolution cameras with object recognition capabilities are essential in low-speed, high-density urban environments where robotaxis typically operate.

Another option is to leverage sensor fusion, combining the strengths of multiple relatively cheap sensors instead of relying on more expensive devices.

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Full driving automation

Long way ahead for full driving automation

While semi-autonomous driving features such as ADAS have long been an integral part of many drivers' daily routines, the most advanced forms of driving automation remain limited or are still largely within the research phase. Progress, however, has been made thanks to advances in both sensor technology and artificial intelligence software. In fact, recent statistics are rather promising, with crash rate benchmarks proving that self-driving vehicles like Waymo's robotaxis can outperform the average human driver.

This gradual increase in performance could help overcome a major barrier to large-scale adoption, convincing a still highly skeptical public that self-driving cars aren't more dangerous than any other traditional vehicle. Changing this perception will take time. In the meantime, automakers can focus on the technical challenges of implementing autonomous driving technology, potentially with support from an experienced IT partner like Itransition.

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