Object detection and subsequent perception of the environment surrounding a vehicle play a very important role in autonomous driving applications. Existing perception algorithms do not generalize well since most algorithms are trained in well-structured driving environment datasets. To deploy self-driving cars on the road, they should have a reliable and robust perception system to handle all corner cases. This paper introduces a multimodal dataset, TIAND* (TiHAN-IITH Autonomous Navigation Dataset), collected from structured and unstructured environments seen in and around the city of Hyderabad, India, as an aid to further research in the generalization of object detection algorithms. The sensor suite contains four cameras, six radars, one Lidar, and GPS and IMU. TIAND comprises 150 scenes, each spanning a duration ranging from 2 minutes to 4 minutes. Subsequently, we present the object detection model’s performance using camera, radar, and Lidar data. Additionally, we offer insights into projecting data from Lidar to camera and from radar to camera.
TIAND: A Multimodal Dataset for Autonomy on Indian Roads
2024-06-02
2196003 byte
Conference paper
Electronic Resource
English
Engineering Index Backfile | 1943
British Library Online Contents | 2001
|Interurban trolley roads in Indian
Engineering Index Backfile | 1904
The Indian Reservation Roads Program
British Library Conference Proceedings | 2007
|Indian Roads Congress: The Vision Ahead!
British Library Online Contents | 2001
|