LiDAR-Based Predictive Cruise Control FEV North America Incorporated

Format:

Book

Conference/Event

Author/Creator:

Alzu'bi, Hamzeh, author.

Contributor:

Alrousan, Qusay

Tasky, Tom

Conference Name:

WCX SAE World Congress Experience (2020-04-21 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2020

Summary:

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, calculate grade of the road, and plan the route in drivable areas. This information is critical for the PCC to define the optimal speed profile of the vehicle while following the planned path. 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. Test results show that the proposed PCC was implemented successfully, allowing the vehicle to adapt its speed based on the processed data of the LiDAR sensor

Notes:

Vendor supplied data

Publisher Number:

2020-01-0080

Access Restriction:

Restricted for use by site license