Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions

By R. Rasshofer, K. Gresser. 2005

Abstract. Automotive radar and lidar sensors represent key components for next generation driver assistance functions (Jones, 2001). Today, their use is limited to comfort applications in premium segment vehicles although an evolution process towards more safety-oriented functions is taking place. Radar sensors available on the market today suffer from low angular resolution and poor target detection in medium ranges (30 to 60m) over azimuth angles larger than ±30°. In contrast, Lidar sensors show large sensitivity towards environmental influences (e.g. snow, fog, dirt). Both sensor technologies today have a rather high cost level, forbidding their wide-spread usage on mass markets. A common approach to overcome individual sensor drawbacks is the employment of data fusion techniques (Bar-Shalom, 2001). Raw data fusion requires a common, standardized data interface to easily integrate a variety of asynchronous sensor data into a fusion network. Moreover, next generation sensors should be able to dynamically adopt to new situations and should have the ability to work in cooperative sensor environments. As vehicular function development today is being shifted more and more towards virtual prototyping, mathematical sensor models should be available. These models should take into account the sensor's functional principle as well as all typical measurement errors generated by the sensor.

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Citations in the corpus

A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “An easy alternative is to use an alternate sensing modalities for perception, but lidar also has difficulties with some weather conditions like snow [144],”
MmWave Beam Prediction with Situational Awareness: A Machine Learning Approach (2018, background): “In addition, connectivity between vehicles allows exchange of such information, which equips the vehicles with situational awareness [10], [11].”
Predicting the Influence of Rain on LIDAR in ADAS (2019, background): “Although the qualitative impacts of weather on these sensors has been studied for quite some time [2], there has been surprisingly little progress in quantitatively predicting the impact of rain on LIDAR sensors typically used in ADAS systems.”
Automotive Radars: A review of signal processing techniques (2017, background): “Moreover, lidar is more sensitive to environmental factors such as snow, fog, dust, and rain [64].”
An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions (2021, unknown): “Due to their limited coverage range (less than 2 m) and poor angular sensing resolution, they do not obtain consistent information about the location and the velocity of vehicles on the road [61].”