How Data Annotation Powers Autonomous Mobility

Today, a majority of automobile manufacturers (Tesla, GM, Ford, BMW, Toyota, PSA, Renault-Nissan) and companies such as Waymo, Uber, NuTonomy envision a future with robotic vehicles driving around without any human presence being necessary. It is estimated that in 2025, the installation rate of AI-based systems in new cars should increase by 109%. Here are some of the top features and data innovations powering autonomous vehicles, along with the data annotation required to make them possible.

The Evolution of Data Annotation in Autonomous Vehicles

The autonomous driving industry relies on robust datasets to train and validate perception systems. Recent advancements have produced specialized datasets addressing specific challenges in autonomous mobility:

RoboSense: Multi-Sensor Dataset for Low-Speed Autonomous Driving targets delivery robots and street sweepers with 133K+ synchronized frames and 1.4M 3D bounding boxes across 6 scenario types, addressing near-field perception challenges unique to low-speed vehicles.
MAN TruckScenes: Multi-Modal Dataset for Autonomous Trucks is the first large-scale dataset specifically designed for heavy vehicles, containing 747 scenes with 4D radar data and 27 object categories covering truck-specific challenges like trailer occlusion and logistics terminal environments.
Para-Lane: Cross-Lane Novel View Synthesis Dataset provides 16,000 front views and 64,000 surround views from real-world multi-lane driving, enabling validation of parallel path planning for lane changes and emergency maneuvers.
UniOcc: Unified Occupancy Forecasting Benchmark combines real-world data from nuScenes and Waymo with simulated environments, introducing voxel-level flow annotations that enable motion prediction at the occupancy level for navigation decision-making.
Adver-City: Adverse Weather Collaborative Perception Dataset recreates dangerous road configurations with 24K frames across six weather conditions, including the first simulation of blinding glare, addressing the 70% higher accident risk in rainy conditions.

Inside the Cabin: Monitoring Driver and Occupant Behavior

In-cabin monitoring has gained wide popularity and has seen significant advancement. In simple terms, in-cabin monitoring is the placement of cameras and vision systems internally to monitor the driver and other occupants. In-Car sensors can enable monitoring of vehicle occupants for levels of drowsiness and distraction by observing head and body position as well as eye gaze.

Example – The Mood Detector in Jaguar Land Rover identifies the smallest variations in the driver’s facial expressions and interacts in real-time to optimize the parameters of their comfort.

Data Annotation: Keypoint annotation for detecting driver distraction and monitoring in-cabin behaviour

Enhancing Road Safety: Real-Time Road Damage Detection

A key focus area that has captured the attention of automakers and safety regulators is improving road safety. Previous works on road damage identification have tackled the detection and classification of individual types and classes, and only very recently, some works have addressed the problem of detecting multiple classes and instances in real time, which usually requires the use of modern Deep Learning (DL)-based detectors or semantic segmentation schemes.

Data Annotation: Semantic Segmentation, Thermal Image annotation for detecting road damages

Voice-Enabled Mobility: Automating In-Car Assistants

Car manufacturers observed the trend of using mobile devices for navigation as an opportunity to improve passenger experience and started integrating sophisticated voice-enabled assistants in their vehicles. Voicebot’s report revealed a 13.7% growth in users of in-car voice assistants from September 2018 to January 2020. As voice assistant adoption continues to climb, so does the need for good quality training datasets to allow a smooth experience.

Example – The new Lamborghini Huracán Evo leverages Alexa to control environmental settings, including air conditioning, heater, fan speed, temperature, seat heaters, defroster and air flow direction, as well as lighting. The voice assistant’s AI can intuit what people mean from less direct requests too, turning on heat or AC when the driver says they are too hot or cold, for instance.

Data Annotation: NLP Services

Personalizing the Journey: Infotainment Systems and AI

Car infotainment systems are able to connect with smart technologies such as smartphones, telematics devices, sensors, and more to provide a personalized experience and predictive services to drivers and passengers alike. In 2016, the in-vehicle infotainment systems market generated revenue of 33.78 billion U.S. dollars worldwide, a figure which is forecast to increase to over 52.2 billion U.S. dollars by 2022.

Example – Nuance Communication has announced that its Dragon Drive artificial intelligence (AI) platform for the connected car now features expanded conversational and cognitive capabilities for its Automotive Assistant to provide everyone in the car with the ability to ask for navigation, music, content and other in-car features just by speaking without any wake-up phrase or button press required.

Data Annotation: NLP Services

Precision Mapping: HD Maps and Localization Technologies

By identifying a vehicle’s exact position in its environment, localization is a critical prerequisite for effective decisions about where and how to navigate. HD Mapping uses onboard sensors (including GPS) to compare an AV’s perceived environment with corresponding HD maps. It provides a reference point the vehicle can use to identify, on a very precise level, exactly where it is located (including lane information) and what direction it’s heading toward.

Example – HERE HD Live Map uses machine learning to validate map data against the real world in real time.

Innovative datasets critical to precision mapping include:

DurLAR: High-Fidelity 128-Channel LiDAR Multi-Modal Dataset pushes beyond typical 16-64 channel LiDAR systems with 128 channels, producing 2048×128 panoramic images in both ambient and reflectance modes. With 100,000 frames under varying conditions, this dataset enables fine-grained scene understanding for precise localization.
V2X-Radar: 4D Radar Cooperative Perception Dataset is the world’s first large-scale vehicle-to-infrastructure cooperative perception dataset containing 4D radar, providing 20K LiDAR frames, 40K camera images, and 20K 4D radar frames with 350K annotated boxes for all-weather autonomous driving.
V2X-R: Cooperative LiDAR-4D Radar Fusion Dataset offers controlled simulation environments with 12,079 scenes and 150,908 images, emphasizing adverse weather simulation, including fog and snow. Multi-modal denoising modules leverage 4D radar to clean noisy LiDAR data in challenging conditions.

Data Annotation: Lidar and Radar Annotation for object detection

Advancing Safety: Automated Emergency Braking and Collision Prevention

Advanced safety systems using AI are being delivered in cars today, whether the customer asks for them or not. This safety feature is essential for fully autonomous vehicles as it automatically stops the vehicle to avoid a collision. Research conducted by General Motors on Advanced Driver Assistance Systems with the University of Michigan Transportation Research Institute, showed that Automatic Emergency Braking (or Forward Automatic Braking) with Forward Collision Alert reduced rear-end striking crashes by 46%.

The latest datasets that support saftey in autonomous mobility include:

AIDOVECL: AI-Generated Vehicle Outpainting Dataset addresses limitations in traditional vehicle datasets through synthetic generation. Starting with 15,000 seed images, the system uses diffusion models to create precise bounding boxes for 9 vehicle categories, filling the gap in eye-level vehicle detection.
TLD: Vehicle Taillight Signal Dataset is the first large-scale taillight signal detection dataset with 152,690 annotated frames and 307,509 instances covering day/night and various weather conditions, enabling autonomous vehicles to predict driver intentions through turn signal recognition.
OpenAD: Open-World Autonomous Driving 3D Object Detection Benchmark addresses edge cases with 2,000 scenes from five major datasets, annotating 6,597 edge case objects across 206 categories, including construction zones, fallen cargo, and unusual vehicles for handling unexpected scenarios.

Data Annotation: Annotation on vehicles, pedestrians and objects on road

Advanced Perception and AI-Powered Decision Making

Modern autonomous vehicles require sophisticated perception systems that can understand complex driving scenarios and make intelligent decisions in real-time:

DriveLMM-o1: Step-by-Step Reasoning Dataset is the first comprehensive dataset designed specifically for structured reasoning in autonomous driving. With over 18K training samples covering perception, prediction, and planning tasks, it enables models to provide explicit reasoning processes for driving decisions.
CoVLA: Comprehensive Vision-Language-Action Self-Driving Dataset unites visual understanding, language descriptions, and precise action planning. Built on over 80 hours of Tokyo driving data with 10,000 scenes, it integrates multi-sensor information with natural language descriptions and trajectory annotations.
WayveScenes101: Autonomous Driving Novel View Synthesis Dataset provides 101 driving sequences totaling 101,000 images from diverse US and UK locations, specifically evaluating off-axis view generation essential for understanding scenes during lane changes and emergency maneuvers.
SEVD: Synthetic Event-based Vision Dataset is the first synthetic event camera vision dataset for autonomous driving. Built in CARLA with 58 hours of multi-modal data and 9 million bounding box annotations, it provides RGB, depth, optical flow, and semantic segmentation alongside event streams for emerging sensor technology research.

Drive Innovation with iMerit’s Data Annotation Solutions for Autonomous Mobility Systems

iMerit’s data annotation services combine cutting-edge technology with human expertise to deliver the precise, high-quality training data that powers autonomous mobility systems. We specialize in multi-sensor fusion annotation across LiDAR, radar, and camera inputs, enabling our clients to build perception models that accurately reflect real-world driving complexity. From HD mapping and 3D point cloud labeling to in-cabin monitoring and edge case solutioning, our domain-specialized workforce handles even the most nuanced annotation challenges at scale.

With AI-powered automation, rigorous multi-stage quality assurance workflows, and real-time dashboards for actionable intelligence, we accelerate your path from raw sensor data to production-ready datasets. Contact our experts today to learn how we can power your autonomous mobility innovation.