Prashanth Viswanath

High Performance Front Camera ADAS Applications on TI's TDA3X Platform

Advanced driver assistance systems (ADAS) are designed to increase drivers situational awareness and road safety by providing essential information, warning and automatic intervention to reduce the possibility/severity of an accident. Of the various types of ADAS modalities available, camera based ADAS are being widely adopted for their usefulness in varied applications, overall reliability and adaptability to new requirements. But camera based ADAS also represents a complex, high-performance, and low-power compute problem, requiring specialized solutions. This paper introduces a high performance front camera ADAS based on a small area, low power System-on-Chip (SoC) solution from Texas Instruments called Texas Instruments Driver Assist 3x (TDA3x). The paper illustrates compute capabilities of the device in implementation of a typical front camera ADAS. The paper also introduces key programming concepts related to heterogeneous programmable compute cores in the SoC and the software framework to use those cores in order to develop the front camera solutions. These aspects will be of interest not only to the ADAS developers but for computer vision and compute intensive embedded system development.

ORB in 5 ms: An Efficient SIMD Friendly Implementation

One of the key challenges today in computer vision applications is to be able to reliably detect features in real-time. The most prominent feature extraction methods are Speeded up Robust Features(SURF), Scale Invariant Feature Transform(SIFT) and Oriented FAST and Rotated BRIEF(ORB), which have proved to yield reliable features for applications such as object recognition and tracking. In this paper, we propose an efficient single instruction multiple data(SIMD) friendly implementation of ORB. This solution shows that ORB feature extraction can be effectively implemented in about 5.5 ms on a Vector SIMD engine such as Embedded Vision Engine(EVE) of Texas Instruments(TI). We also show that our implementation is reliable with the help of repeatability test.

A Diverse Low Cost High Performance Platform for Advanced Driver Assistance System (ADAS) Applications

Advanced driver assistance systems (ADAS) are becoming more and more popular. Lot of the ADAS applications such as Lane departure warning (LDW), Forward Collision Warning (FCW), Automatic Cruise Control (ACC), Auto Emergency Braking (AEB), Surround View (SV) that were present only in high-end cars in the past have trickled down to the low and mid end vehicles. Lot of these applications are also mandated by safety authorities such as EUNCAP and NHTSA. In order to make these applications affordable in the low and mid end vehicles, it is important to have a cost effective, yet high performance and low power solution. Texas Instruments (TIs) TDA3x is an ideal platform which addresses these needs. This paper illustrates mapping of different algorithms such as SV, LDW, Object detection (OD), Structure From Motion (SFM) and Camera-Monitor Systems (CMS) to the TDA3x device, thereby demonstrating its compute capabilities. We also share the performance for these embedded vision applications, showing that TDA3x is an excellent high performance device for ADAS applications.

A robust and real-time image based lane departure warning system

Lane departure warning (LDW) systems have gained a lot of interest over the last few years. EuroNCAP regulations mandate European car makers to have LDW system to get star rating. In this paper, we propose a simple image-based implementation of LDW system optimized for the Texas Instruments (TI) C66x Digital Signal Processor (DSP). We show that our solution is able to overcome certain challenges faced in prior methods and consumes only 2.3 mega cycles/frame of the DSP, leaving the rest of the DSP for other applications. We also share some results of our implementation.

Improved ground plane detection in real time systems using homography

Advanced Driver Assistance Systems (ADAS) are used to assist driver by providing real-time information about cross traffic, parking space and so on. Such applications require a fair amount of processing to be done on the incoming video feed. It has been proved that the homography approach is efficient for ground plane detection. But, in the case of Road Plane Detection, the lane markings appear as obstacles when the SAD approach [1] is used. We propose a solution to this problem which involves using a Road Model and applying lane masking. We are able to get an accuracy of 88% to 96%. This technique is suitable for real time applications and we also provide benchmarks on a DSP processor, TMS320C6000 for the key functions used in this algorithm.

Real time Structure from Motion for Driver Assistance System

Understanding of 3D surrounding is an important problem in Advanced Driver Assistance Systems (ADAS). Structure from Motion (SfM) is well known computer vision technique for estimating 3D structure from 2D image sequences. Inherent complexities of the SfM pose different algorithmic and implementation challenges to have an efficient enablement on embedded processor for real time processing. This paper focuses on highlighting such challenges and innovative solutions for them. The paper proposes an efficient SfM solution that has been implemented on Texas Instruments TDA3x series of System on Chip (SoC). The TDA3x SoC contains one vector processor (known as EVE) and two C66x DSPs as co-processors which are useful for computationally intensive vision processing. The proposed SfM solution which performs Sparse Optical Flow, Fundamental matrix estimation, Triangulation, 3D points pruning consumes 42% of EVE and 10% of one DSP for 25 fps processing of one mega pixel image resolution.