The narrative surrounding artificial intelligence has reached a saturation point, with numerous players flooding the marketYet amidst this noise, DeepSeek has emerged as a game changer, stirring up the landscape of intelligent driving technology in a significant wayOn January 20th, the company made waves by unveiling its DeepSeek-R1 model, accompanied by the open-source release of its model weightsThis move, characterized by a surprisingly low cost and impressive performance, led to an astonishing user growth of over 100 million within just a week, propelling it into the realm of global phenomenon.

Fast forward to December 2024, DeepSeek announced the launch of its advanced multimodal understanding model, the DeepSeek-VL2. This hybrid visual-language model promises enhanced capabilities in various aspects, including visual question answering, optical character recognition, document/table/chart comprehension, and visual localization, showcasing a significant leap in multimodal processing.

Interestingly, even before DeepSeek gained widespread attention, industry experts had begun taking notice of the potential applications of its modelsSeveral autonomous driving companies were treadling ahead, exploring how DeepSeek’s technology could transform their operationsAccording to a top executive from a leading player in the autonomous vehicle sector, the initial tests conducted at the beginning of the year yielded results that exceeded expectationsHe highlighted a key improvement: the model's trainability has dramatically increased operational efficiency, cutting inference response times by 40% to even 50%, while also optimizing computational resource utilization.

In the eyes of industry insiders, DeepSeek, as an open-source foundational model, is primed to accelerate the training speed of intelligent driving technologies and significantly reduce associated costsChristopher Zhang, the systems director at Zhixing Automotive Technology, pinpointed the challenging complexity of urban environments as a significant hurdle in system development

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Traditional perception models struggle to address long-tail scenarios, and companies are keen on developing and training visual language models (VLMs) to enhance detection and response capabilities for these unique situations.

However, creating such a system demands considerable cloud computing power and incurs high data training costs, which can be burdensome when deploying models onto hardware with stringent computational requirementsHere, DeepSeek's unique technological edge comes into play, featuring innovations such as the Mixture of Experts (MoE) architecture, group relative policy optimization (GRPO), and a multi-head latent attention mechanism (MLA) that can effectively empower the development of intelligent driving systems.

In a nutshell, Zhang posited that DeepSeek enables equivalent urban autonomous driving functionality with less data and reduced training costsDelving deeper into the cloud training phase, the training of autonomous driving models emerges as a complex venture requiring extremely high data qualityThis training data needs to encompass a rich variety of scenarios, including different road conditions, weather variations, and traffic contextsRaw data in its nascent form is akin to unrefined gems that require meticulous labeling for transformation into meaningful inputs the model can learn fromFor instance, data collected from roadway imagery mandates precise annotation detailing vehicle types, locations, directions, pedestrian postures and movements, as well as the statuses of traffic signs and signalsOnly after such thorough annotation can this data be fed into the model to train it to accurately identify vehicles and pedestrians and judiciously assess various traffic situations.

DeepSeek’s unique technical advantages shine in this demanding processIts framework minimizes the need for exhaustive data tagging, thus liberating intelligent driving companies from the burdensome manual labor typically associated with extensive data annotation

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By harnessing its advanced algorithms, DeepSeek excels at probing and analyzing raw data, automatically identifying key features and patterns to produce high-quality training datasetsFor example, it can deploy machine learning techniques to sift through substantial volumes of roadway imagery, extracting representative scenes and elements without necessitating the painstaking manual annotation traditionally involvedThis automated approach not only skyrockets the efficiency of data generation but simultaneously curtails the financial costs of data collection and annotationAs a result, autonomous driving enterprises can redirect resources away from the data annotation phase and focus more on model optimization and algorithm innovation, accelerating technological advancement and enhancing the competitiveness of their offerings.

On the vehicle side, DeepSeek employs distillation techniques to bolster the model's capability while minimizing the computational resource requirements at the car's endThis results in significant reductions in both the computational power needed for single-instance model calls as well as the associated training costsIn terms of contextual understanding, Zhang underlined that DeepSeek facilitates enhanced logical reasoning and scene comprehension ability through cross-modal transferIts performances in extreme road conditions, such as dead ends, rare traffic sign recognition, and sudden roadwork disruptions, are projected to surpass those of traditional models.

Yang Yuxin, CMO of Black Sesame Intelligence, echoed this sentiment, speculating that DeepSeek could soon integrate a multitude of data streams—visual, auditory, and environmental—into a more human-like decision-making framework for drivingFuture advancements might see systems dynamically recalibrating route navigation at complex intersections or rapidly establishing safety protocols following unexpected occurrences.

Yang opined that the pivotal value of large models like DeepSeek lies in their capacity for efficient on-device inference, which helps propel intelligent driving systems from a perception-based foundation towards a robust cognitive-driven approach

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