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An outlook is also given on data fusion aspects between this Lidar sensor and camera systems, which further improve functionality and availability of the safety functions.

This contribution deals with the development of a new scanning time-of-flight Lidar device for automotive use. Originally designed for safety applications, it provides a horizontal field of view of up to 170°. ...A detection range of up to 170 m enables tasks such as Full Speed Range ACC (FSRA), thanks to its technology based on a production proven IDIS® 1.0 circuit with Multibeam Lidar. A brushless DC Motor and a small momentum of inertia ensure the precision and sturdiness required for automotive applications.

Multiple object detection and tracking are central aspects of modeling the environment of autonomous vehicles. Lidar is a necessary component in the autonomous driving system. Without Lidar sensors, we will most probably not see fully self-driving cars become a reality. ...In advanced driver assistance systems or automated driving systems, 3-D point clouds from lidar scans are typically used to measure physical surfaces. Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. ...Lidar is a powerful sensor that you can use in challenging environments where other sensors might prove inadequate. Lidar can provide a complete 360-degree view of a scene. This paper designs Lidar based multi-target detection and tracking system based on the traditional point cloud processing method including down-sampling, denoising, segmentation, and clustering objects.

This paper describes a two-dimensional scanning Lidar sensor technology that has 30 degree horizontal and 10 degree vertical fields of view (FOV).

The paper describes the implementation of Adaptive Range LIDAR Systems (ARLS) containing a state of the art collimator and wave shaper with a 140̊ sweep MEMS mirror, capable of calculating beam convergence as a function of distance, considering multiple obstacles ahead of it.

Advanced Driver Assistance Systems (ADAS) enable safer driving by relying on the inputs from various sensors including Radar, Camera, and LiDAR. One of the newly emerging ADAS features is Predictive Cruise Control (PCC). PCC aims to optimize the vehicle’s speed profile and fuel efficiency. ...This paper presents a novel approach of using the point cloud of a LiDAR sensor to develop a PCC feature. The raw point cloud is utilized to detect objects in the surrounding environment of the vehicle, estimate the grade of the road, and plan the route in drivable areas. ...This paper also discusses the developed algorithms of the LiDAR data processing and PCC controller. These algorithms were tested on FEV’s Smart Vehicle Demonstrator platform.

Renault and other partners involved in the Urban Drive Control European project also implemented Stop&Go using radar and lidar sensors. This mode which could also include lateral control would be more useful to the driver faced with recurrent congestion.

Flash! Lidar's Next Generation Arrives Technology solutions from new players and alliances are poised to drive down cost.

In addition, in this paper we have presented different type of labelling methodology for camera, radar and Lidar sensor data which are being used for automotive drive data.

Radar and Camera or Lidar sensor based functions of AEB (Automated Emerge Brake), ACC (Automated Cruise Control) or LDW (Lane Departure Warning) might be invalid, especially when these sensors were illuminated by electrical disturbances in the real word.

These active safety features present in developed markets work with Fusion based algorithm combining Radar, Lidar, Camera, Ultrasonic sensor’s input. Application of these algorithms are Intelligent Cruise Control, Collision avoidance, parking assistance, identify pedestrian etc.

More than 90% of new vehicles include Advanced Driving Assistance Systems that offer features such as Lane Keep Assist and Adaptive Cruise Control [1]. These ever-improving vehicle systems present a great opportunity to increase driving safety and reduce the number of roadway deaths and injuries. Indeed, they are already having a positive effect. However, the wide variety of features offered in the marketplace can be confusing to consumers, who may not clearly understand their vehicles’ true capabilities and limitations, or have an easy way of comparing system performance between vehicle models. This lack of information has the potential to reduce the safety gains of ADAS features by increasing the risk of improper use. To encourage transparency in the marketplace and thus engender the maximum positive effect of ADAS technologies, this paper proposes a five-level rating system, which utilizes diamonds to denote significant milestone achievements in vehicle system performance.

Since the late 1980s, Delphi Automotive Systems has been very involved with the practical development of a variety of Collision Avoidance products for the near- and long-term automotive market. Many of these complex collision avoidance products will require the integration of various vehicular components/systems in order to provide a cohesive functioning product that is seamlessly integrated into the vehicle infrastructure. One such example of this system integration process was the development of an Adaptive Cruise Control system on an Opel Vectra. The design approach heavily incorporated system engineering processes/procedures. The critical issues and other technical challenges in developing these systems will be explored. Details on the hardware and algorithms developed for this vehicle, as well as the greater systems integration issues that arose during its development will also be presented.

The model vehicle is equipped with common ADAS sensors, such as optical cameras and LiDARs, which are known to be heavily affected by adverse weather. Quantification metrics are designed and applied to ADAS datasets to investigate sensor performance in conjunction with related phenomena, such as the perceived rain characteristics of a moving vehicle.

The use of very short wavelength radar sensors for closed loop adaptive cruise control (ACC) and obstacle detection has been demonstrated as part of various international research and technology demonstration programs (e.g. PROMETHEUS in Europe). The first radar systems for distance measurement and target tracking are now under development, with vehicle trials already under way in several countries. This paper describes a 77 GHz radar sensor which will begin production next year (1999) as a headway sensor, initially for use in cruise control systems. The challenges facing the sensor manufacturer include the development of robust product designs and process technologies suited to the high volume and reliability demands of the automotive market, whilst achieving this at a sustainable production cost. The key design features of such a product are described, together with the major factors influencing cost and performance.

This paper will address the basic requirements for realizing a stop and go cruise control system. Issues discussed comprise: functional, sensor and basic HMI requirements, primary characterization of naturalistic stop and go driving, and the basic approach of the transformation of situational knowledge in an elementary controller.