Artificial Intelligence in Cars: How Close Yet Far Are We from Fully Autonomous Vehicles?

Palade, Vasile; Deo, Ankur

doi:10.1142/s0218213022410056

Cited by 3 publications

(2 citation statements)

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“…It is understood that all ADAS algorithms possess unique advantages and disadvantages, and the best choice of algorithms is defined by the requirement of the feature [41,46]. For most ADAS algorithms, maintaining consistency across all external scenarios is critical [47]. Following the critical analysis of the fusion architecture based on the switching tracker mechanism, it must be noted that by using algorithms like the TSR and the RTDE, we include certain shortcomings.…”

Section: Discussionmentioning

confidence: 99%

Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Deo

Palade

2022

Electronics

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Modern cars utilise Advanced Driver Assistance Systems (ADAS) in several ways. In ADAS, the use of multiple sensors to gauge the environment surrounding the ego-vehicle offers numerous advantages, as fusing information from more than one sensor helps to provide highly reliable and error-free data. The fused data is typically then fed to a tracker algorithm, which helps to reduce noise and compensate for situations when received sensor data is temporarily absent or spurious, or to counter the offhand false positives and negatives. The performances of these constituent algorithms vary vastly under different scenarios. In this paper, we focus on the variation in the performance of tracker algorithms in sensor fusion due to the alteration in external conditions in different scenarios, and on the methods for countering that variation. We introduce a sensor fusion architecture, where the tracking algorithm is spontaneously switched to achieve the utmost performance under all scenarios. By employing a Real-time Traffic Density Estimation (RTDE) technique, we may understand whether the ego-vehicle is currently in dense or sparse traffic conditions. A highly dense traffic (or congested traffic) condition would mean that external circumstances are non-linear; similarly, sparse traffic conditions would mean that the probability of linear external conditions would be higher. We also employ a Traffic Sign Recognition (TSR) algorithm, which is able to monitor for construction zones, junctions, schools, and pedestrian crossings, thereby identifying areas which have a high probability of spontaneous, on-road occurrences. Based on the results received from the RTDE and TSR algorithms, we construct a logic which switches the tracker of the fusion architecture between an Extended Kalman Filter (for linear external scenarios) and an Unscented Kalman Filter (for non-linear scenarios). This ensures that the fusion model always uses the tracker that is best suited for its current needs, thereby yielding consistent accuracy across multiple external scenarios, compared to the fusion models that employ a fixed single tracker.

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Section: Discussionmentioning

confidence: 99%

Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Deo

Palade

2022

Electronics

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“…Many organizations use AI as a foundation to automate repetitive processes and to gather valuable data to make sense of information that will be useful for the decision-making process throughout all project phases. This makes an organization's operations run efficiently and allows business executives to make effective decisions (Palade & Deo, 2022). However, the main questions to be asked is, what do all business executives need to know about AI in order to build or grow an organization that is reliant on AI, and how much should they know about the advantages and constraints of AI in order to make use of it at its full capacity (Habbal et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Impact of AI TRiSM on Knowledge and Decision Making for Business Executives in the Education Industry

El Khatib,

Al Sharif,

Mohamad

2024

Int. J. TOP

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Artificial intelligence, also referred to as AI, allows operations to run efficiently, gathers important and relevant data, and self-analyses trends for accurate decision making. This research paper focuses on exposing the impact of AI TRiSM in knowledge and decision making for business executives in the education industry. The objectives are to investigate the impacts of AI TRiSM on business executives in knowledge and decision making in the education industry, and to explore how we can maximize the positive impacts and minimize the negative impacts of AI TRiSM on business executives in knowledge and decision making in the education industry. The problem to be identified it that although AI carries many advantages, not many are fully aware of its capabilities. The hypotheses to be investigates is that H1: E-education tools will improve student engagement and enhance learning outcomes compared to the traditional method of learning, and H2: Business executives and educators’ workload will be reduced because of AI introduction to the education industry. Various research questions will be asked to state the truthfulness and accuracy of both hypotheses. Secondary and primary data has been gathered to share sufficient insights around the topic. AI is all about humanizing technology. However, as much as we want to replicate human intelligence, AI is still at its infancy to think and feel like a human. AI TRiSM guarantees the robustness, fairness, reliability, effectiveness, privacy, and data protection of AI models. Many companies have adopted AI such as Google, Netflix, and Tesla. In the education industry, the introduction of AI has several advantages such as personalized learning and automation of tasks. However, it does have its disadvantages such as high cost and privacy concern. Several cases around the globe (UAE, China, and USA) have been reflected as well to highlight how AI has been applied in education. Through primary data gathering, 2 interviews have been conducted. 1 from the student perspective, and the other from the educator perspective, and both were asked 6 questions during the interview. From those responses, data was analyzed. Limitations of conducting this research paper has been addressed, followed by recommendations of adopting AI technologies, and finally a conclusion that summarizes the whole research paper and reflects on the accuracy of both hypotheses stated.

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Safe and efficient manoeuvring for emergency vehicles in autonomous traffic using multi-agent proximal policy optimisation

Parada

Candela

Marqués

et al. 2023

Transportmetrica A: Transport Science

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Artificial Intelligence in Cars: How Close Yet Far Are We from Fully Autonomous Vehicles?

Cited by 3 publications

References 0 publications

Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems

Impact of AI TRiSM on Knowledge and Decision Making for Business Executives in the Education Industry

Safe and efficient manoeuvring for emergency vehicles in autonomous traffic using multi-agent proximal policy optimisation

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