Akkodis

The role will be based in Auburn Hills, MI but is open to fully remote candidates who live in the state of Michigan.. In the ever-changing realm of autonomous driving technology, the forefront of innovation is marked by the development of cloud-enabled autonomous systems.. By repurposing the lidar-based system in this manner, it becomes an integral component of the roadside infrastructure, strengthening the functionality and effectiveness of autonomous driving technologies.. Refine the system's performance to address specific situations such as autonomous vehicle responses to pedestrians crossing at designated crosswalks and traffic signal interactions.. The system involves: i) Establishing the connection between the vehicle system, infrastructure sensor system, and smart traffic lighting system; ii) Developing essential software capable of logging and replaying data to showcase specific scenario-based offline vehicle demonstrations, including AV yielding to vulnerable road users crossing the road and AV reactions to traffic signals.

development

Akkodis is seeking a candidate for a Connected Autonomy System Algorithm Development position with one of our Fortune 500 partners in the Automotive industry.The top candidate would have experience working with Python libraries, object detection algorithms, robotics, sensors, LiDAR, computer vision, etc. , preferably for automotive related projects.This is a long-term role with competitive pay, benefits and work life balance. The role will be based in Auburn Hills, MI but is open to fully remote candidates who live in the state of Michigan.Pay/Salary Range: $45-55/hrJob Description:Connected Autonomy System DevelopmentIn the ever-changing realm of autonomous driving technology, the forefront of innovation is marked by the development of cloud-enabled autonomous systems. These systems, powered by the capabilities and connectivity of cloud computing, revolutionize how vehicles perceive and respond to their surroundings. Beyond improving environmental awareness, localization, and real-time infrastructure mapping, this integration offers opportunities for instantaneous data exchange and collaboration among vehicles and infrastructure. Within this dynamic landscape, the merging of cutting-edge technologies with cloud-based solutions heralds a new era in self-driving vehicles, paving the way for safer, more intelligent, and highly efficient transportation ecosystems.<ul><li>The supplier will use network-based structures and real-time architecture to develop a comprehensive suite of embedded software solutions. These solutions will aim to address various challenges faced in connected autonomy, including:Detect and extract relevant information from infrastructure sensors. The supplier will evaluate the latest advancements in object detection (e.g., VRU and vehicles) technologies utilized by sensors on infrastructure and incorporate the broadcast of messages to vehicles.</li><li>Precise localization of the self vehicle and alignment of data from infrastructure sensors with its onboard sensors, and create a consistent environmental representation that fuses data from different sources, and tackles the artifacts caused by sensor field of view occlusion, sensor noise, and communication networks latency and jitters.</li></ul>The overall objective of this project is to design and implement an advanced computing framework geared towards connected autonomy vehicles. The envisioned framework encompasses the following key tasks:Object detection on infrastructure camera:Design and implement a robust object detection system utilizing state of the art (SOTA) framework on infrastructure cameras. This involves the creation and optimization of algorithms capable of identifying and categorizing objects in real-time from the visual data captured by these cameras. The development process will encompass training the SOTA model on relevant datasets to ensure accurate and efficient recognition of various objects within the camera's field of view. Additionally, fine-tune the system to enhance its adaptability to different environmental conditions and scenarios, ensuring reliable performance in detecting objects such as vehicles, pedestrians, and obstacles. This initiative aims to leverage SOTA efficiency in object detection to enhance the capabilities of infrastructure cameras, contributing to improved infrastructure awareness and overall system effectiveness.Lidar-based object detection from infrastructure:The task involves modifying the current lidar-based object detection system, originally installed on vehicles, to function as part of the road-side equipment configuration. By repurposing the lidar-based system in this manner, it becomes an integral component of the roadside infrastructure, strengthening the functionality and effectiveness of autonomous driving technologies.Integrate data from onboard sensors with infrastructure awareness information:Develop and deploy a fusion system that generates a cohesive representation of surrounding environments, including scenarios like identifying vulnerable road users crossing lanes where visibility is limited. Refine the system's performance to address specific situations such as autonomous vehicle responses to pedestrians crossing at designated crosswalks and traffic signal interactions.Develop accurate vehicle localization system:A precise vehicle localization strategy is applied to align data from onboard sensors and infrastructure sensors, and synchronize the clock latency and jitter, and thus harnessing the strengths of each source to yield a more accurate and reliable outcome.System integration the abovementioned components and smart traffic lighting system:The system involves: i) Establishing the connection between the vehicle system, infrastructure sensor system, and smart traffic lighting system; ii) Developing essential software capable of logging and replaying data to showcase specific scenario-based offline vehicle demonstrations, including AV yielding to vulnerable road users crossing the road and AV reactions to traffic signals.<ol><li>Enhance overall system performance through tuning and optimizing code, while also documenting the process in a technical report to capture the findings:</li></ol>The task requires systematically conduct experiments to assess the efficacy and efficiency of the developed system. This involves evaluating the system's performance metrics and documenting the findings comprehensively to provide insights into its strengths and areas for improvement. The task encompasses a structured approach to experimentation, evaluation of system performance, and the creation of detailed documentation that serves as a resource for future reference and refinement of the developed solution. *Cannot work on C2C basis* *Visa sponsorship is available for this opportunity*Equal Opportunity Employer/Veterans/DisabledBenefit offerings available for our associates include medical, dental, vision, life insurance, short-term disability, additional voluntary benefits, EAP program, commuter benefits and a 401K plan. Our benefit offerings provide employees the flexibility to choose the type of coverage that meets their individual needs. In addition, our associates may be eligible for paid leave including Paid Sick Leave or any other paid leave required by Federal, State, or local law, as well as Holiday pay where applicable.To read our Candidate Privacy Information Statement, which explains how we will use your information, please visit https://www.modis.com/en-us/candidate-privacy/

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