Arrival just became a publicly-traded company in March after merging with a special purpose acquisition company, or SPAC. Founded in 2015, Arrival is also developing electric delivery vans (with UPS as a customer) and buses. It also has backing from Hyundai and Kia.

Arrival and Uber released a handful of renderings of the new car’s interior and said a final design will be revealed by the end of the year. Between now and then, the companies plan to get some drivers involved in the design process, too.

From the images, we can see that there’s a large horizontal screen mounted to the dashboard, similar to what’s in Tesla’s Model 3 and Model Y (and the forthcoming Model S and Model X refresh), and the steering wheel is also quite similar to what’s found in a Tesla, with just two scroll wheels and no driver display.

Photo Credits: Reuters

However, there are a few subtle differences that Arrival says could enhance the ride-hailing experience for both drivers and passengers. The driver’s seat is ergonomically designed to ease the physical strain of sitting in a car for hours on end. The front passenger seat folds up to create more legroom. There’s a bench-style seat in the rear, which makes it easier to get in and out of the car. There are also small, lighted cubbies and handrails on the inside of each door.

According to Tom Elvidge, Arrival’s senior vice president of mobility, their goal is:

“to make hundreds of small improvements, changes, and tweaks to the design that perhaps haven’t been applied before.”

Electric vehicles tend to have fewer moving parts than internal combustion cars and therefore require less maintenance and upkeep. They’re also inherently compliant with the zero-emission zones that have been adopted in cities across Europe, as well as broader internal combustion bans that are on the horizon. Elvidge also added that their goal is to design an affordable vehicle from the outset.

Arrival has yet to prove that strategy can work. Like many of its SPAC peers, it is still in the developmental phase and has yet to actually sell any production vehicles. Uber has quietly poked at a number of electric vehicle startups over the last few years but never struck any deals, though it has recently cleaved a number of money-losing divisions, like the one focused on autonomous vehicles and the one dedicated to flying taxis.

Autonomous vehicles rely on three main technologies, the first of which is high-precision localization, robust and reliable environment perception and last but not least the prediction and motion planning algorithms for safe manoeuvres. The motion planner is easier to achieve as long as the accuracy of the localization and perception data is higher.

Photo Credits: Qualcomm

• The unified localization solution

An autonomous vehicle must be able to localize itself within a few centimetres of accuracy to plan and execute complex manoeuvres required in real-world road conditions. A way to reach higher levels of accuracy is by HD Maps. Qualcomm shared:

“Our approach is to develop advanced and optimized modules that solve such challenges for our automotive OEM customers and partners.”

One such component is Qualcomm Vision Enhanced Precise Positioning (VEPP) software, which can provide lane-level positioning and a precise heading in the global coordinates by tightly fusing GNSS, IMU, camera, and vehicle odometry information in real-time and in virtually all environments.

The localization solution fuses the global pose with camera perception and HD map information and also provides an ability to generate a local map, by producing identification of gaps or errors in HD maps on the fly — all while the vehicle can operate in an autonomous mode.

• Novel techniques for efficient environment perception

One of the main focus areas for Qualcomm is to scale environmental perception technologies in a more efficient way.  Their aim is to harness all sensor types including cameras, radar, and lidar, as well as maps to build novel ways of solving environment perception challenges.

There is multisensor data fusion, which includes data association, as well as object-state estimation. Data association is basically the ability of the autonomous vehicle to correlate data from different views. State estimation involves tracking real-world objects and estimates their size, speed, and position relative to the autonomous vehicle itself.

Another Qualcomm’s focus area is the accurate prediction of the pose and tracking the size of objects on the road with 3D estimations. This happens by the use of 2D data from cameras, radar and maps. 3D estimations leveraging both deep learning and concepts from multi-view geometry. Such accurate 3D estimations can enable human-like manoeuvres for the advanced safety of the autonomous vehicle.

• Accelerating autonomous driving solutions

Qualcomm Technologies has been at the forefront of providing connectivity solutions. Building on this solid understanding of the automotive industry, the representatives of Qualcomm are now helping drive some of the biggest innovations in AD.

Their comprehensive frameworks and tools around cross-sensory calibration, active learning, big data management, and automated verification/validation can significantly reduce the time-to-market for autonomous vehicle manufacturers.