Automotive Radar and Lidar Systems for Next Generation Driver Assistance Functions

Abstract

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.

Multi-Modal Data Fusion Techniques and Applications

“…Video, RADAR and GPS signals can be integrated both to monitor an area like in [43] or for safety scopes for example in driver assistance (e.g. [57], [47] and [29]). In the following section the physical architectures to combine the cited sensors are exposed.…”

V2V Communications in Automotive Multi-Sensor Multi-Target Tracking

“…due to signal scattering) • 1 : T with T > 1: a single measurement can be related to more than one target (e.g. no target separation due to limited angular resolution [3])…”

“…lidar, camera) show significant deterioration in fog, rain or snow causing noisy measurements • unintended reflections and scattering on guard rails, buildings, secondary vehicles, etc. causing the occurrence of "ghost vehicles" • poor angular resolution of automotive radar sensors causing undetection of vehicles [3] Some of these errors can be mitigated by using dynamic state estimation and sensor fusion (e.g. camera & radar) [4], e.g.…”

Target state estimation using RCS characteristics for 26GHz short-range vehicular radar

“…Automotive radar is therefore a fast growing application which improves safety and comfort for all road users in all weather. The 24GHz band is temporally allocated and will switch to the 26GHz short-range radars (SRR) and 79GHz middle-range radars (MRR) band until 2016 [1]. Of the various sensing modalities currently being developed for this purpose, radar sensors are particularly well suited, given allweather performance and ability to provide a direct measure of range [2].…”