AI Advancements In The Mobility Space

Details: By Ann Jacob; 01.Jul

ai advancements

AI Advancements In The Mobility Space

In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are the main cause of a large number of motorcycle accidents. Often, static cameras are not available on all the streets, and it is not economically viable. Many inside roads and crowded streets are far from the reach of these cameras.

At IIITH, we have been aiming to solve problems on Indian roads and driving conditions using data-driven technologies. With the help of techniques from machine learning, computer vision, computer graphics, computational sensing, and allied areas, we have been creating solutions and transferring these solutions across diverse practical conditions on roads.

Ann Jacob

June 18, 2025 | Prof C V Jawahar

Driver Intention Prediction

One of the research studies that was undertaken includes early anticipation of driver actions before the onset of a manoeuvre, such as a turn, a lane change, a sudden halt and so on. When you know the driver in front of you is going to take a right turn without indicating with a signal, you anticipate and get ready for it perhaps by slowing down or changing lanes. In order to predict such driver behaviour, CVIT curated a Driving Action Anticipation Dataset (DAAD) that consists of multiple views – both inside the cabin and outside of heterogenous traffic scenarios that include diverse weather, illumination, and driveway conditions. The dataset captures sequences prior to initiating the manoeuvre and during the manoeuvre as well. The research which culminated in a paper titled, “Early Anticipation of Driving Manoeuvres”was presented at the European Conference on Computer Vision 2024. A natural extension to this research was studied on the 2-wheeler where rider intention is anticipated before the onset of the actual manoeuvre. This is more challenging but the ability to predict rider intention enables riders to react to potential incorrect manoeuvres flagged by advanced driver assistance systems (ADAS). The dataset that was curated was submitted to the International Conference on Pattern Recognition 2024 competition on rider intention prediction.

Other Related Research Directions

In the year 2019, Hyderabad received 700 mm rainfall which was 13% over the long term average for the city. It resulted in damaged roads and road infrastructure. Conventional civil engineering methods of surveying damage take forever. The problem statement that arose was whether we could leverage AI to assess structural damage to the roads. We proposed again the low-cost solution of mounting a mobile phone equipped with camera on any vehicle that could be driven around the city capturing the extent of damage and narrowing down on where attention was required. Around the same time, the government approached us to undertake an automatic survey on the extent of tree cover, or in other words, to count the number of trees along certain stretches of roads. Similarly, we extended the object identification to traffic violations. The moment drivers and riders see a traffic light and a surveillance camera mounted on it, they slow down or quickly wear a helmet, etc. The government wanted an unobtrusive way of capturing the violations, with a mobile phone camera mounted on public vehicles which could automatically issue challans, thereby improving traffic discipline and ensuring compliance to traffic rules.

How To Make Roads Safe?

Alongside the above solutions, we began to explore other areas to really understand roads and driver behaviour. For that the AI needed to shift from perception to anticipation, meaning that it could detect what was present and available but it needed to understand what is absent. In other words, we had to move from a reactive to proactive understanding. All the AI algorithms today are able to tell us what is available, but the need of the hour is to predict what will come next – a looking-forward in time mechanism. That involves bringing back humans into the loop and asking the question – Can we predict humans?